(* Be sure to think about intention‐to‐treat (ITT) when writing number (N) in: have trialists already adjusted results?) (** Check whether endpoint or change data have been used)
____________________________________________________ 'Risk of bias' judgements – provide quotation and page number, then judgement
Adequate sequence generation? | Yes / Unclear / No | |
Allocation concealment? | Yes / Unclear / No | |
Blinding of outcome assessment? | Yes / Unclear / No | |
Incomplete outcome data addressed? | Yes / Unclear / No | |
Free of selective reporting? | Yes / Unclear / No | |
Other sources of bias? | Yes / Unclear / No |
Power calculation?
Items to correspond with trial investigators about?
Date contacted investigators:
Protocol first published: Issue 1, 2017
7 November 2016 | Amended | Subgroup analysis extended in‐line with comments in text. |
25 April 2016 | Amended | Background cut down, response to comments completed throughout, new section on how the intervention might work |
2 July 2008 | Amended | Minor update: 31/07/07 |
2 July 2008 | Amended | Converted to new review format. |
2 July 2008 | New search has been performed | This is an update of the 2003 review. |
26 July 2007 | New citation required and conclusions have changed | Substantive amendment |
Professor James Law has overall responsibility for this review. All authors have contributed to the writing of this protocol.
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Office base and support for the review to be carried out during office hours
James Law (JL) ‐ is an author on one included study ( Law 1999 ) and one excluded study in the previous version of this review ( Kot 1995 ), and has published a non‐Cochrane review in this area ( Law 1997 ). For those studies in which JL is involved, the two other authors (JAD and JJVC) will assess the eligibility of studies for inclusion, complete 'Risk of bias' assessments and extract data. JL received £10,000 funding from the Nuffield Foundation for the previous version of this review ( Law 2003a ); the protocol of which was also published ( Law 2003b ). JL is an Editor for CDPLP. Jane A Dennis (JAD) — is the Feedback Editor for CDPLP. Jenna JV Charlton (JJVC) — none known.
This review is coregistered within the Campbell Collaboration ( Law 2005b ), as is the published protocol ( Law 2003c ).
This review supersedes the review by Law J, Garrett Z, Nye C. Speech and language therapy interventions for children with primary speech and language delay or disorder. Cochrane Database of Systematic Reviews 2003, Issue 3. Art. No.: {"type":"entrez-nucleotide","attrs":{"text":"CD004110","term_id":"30320848","term_text":"CD004110"}} CD004110 . DOI: 10.1002/14651858.CD004110 ( Law 2003a ).
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Scientific Reports volume 13 , Article number: 11155 ( 2023 ) Cite this article
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The sound of a person’s voice is commonly used to identify the speaker. The sound of speech is also starting to be used to detect medical conditions, such as depression. It is not known whether the manifestations of depression in speech overlap with those used to identify the speaker. In this paper, we test the hypothesis that the representations of personal identity in speech, known as speaker embeddings, improve the detection of depression and estimation of depressive symptoms severity. We further examine whether changes in depression severity interfere with the recognition of speaker’s identity. We extract speaker embeddings from models pre-trained on a large sample of speakers from the general population without information on depression diagnosis. We test these speaker embeddings for severity estimation in independent datasets consisting of clinical interviews (DAIC-WOZ), spontaneous speech (VocalMind), and longitudinal data (VocalMind). We also use the severity estimates to predict presence of depression. Speaker embeddings, combined with established acoustic features (OpenSMILE), predicted severity with root mean square error (RMSE) values of 6.01 and 6.28 in DAIC-WOZ and VocalMind datasets, respectively, lower than acoustic features alone or speaker embeddings alone. When used to detect depression, speaker embeddings showed higher balanced accuracy (BAc) and surpassed previous state-of-the-art performance in depression detection from speech, with BAc values of 66% and 64% in DAIC-WOZ and VocalMind datasets, respectively. Results from a subset of participants with repeated speech samples show that the speaker identification is affected by changes in depression severity. These results suggest that depression overlaps with personal identity in the acoustic space. While speaker embeddings improve depression detection and severity estimation, deterioration or improvement in mood may interfere with speaker verification.
Introduction.
Major depressive disorder, also known as depression, is a common mental disorder and a leading cause of disability worldwide 1 . According to the World Health Organization 2 , more than 300 million people (around \(5\%\) of the global population) are living with depression. Early and objective diagnosis of depressive symptoms is crucial in reducing the burden of depression, but inadequate access to clinical services and associated stigma limit detection. In addition to depression identification, it is important to measure the severity of depression as repeated measurements are needed to guide effective treatment and improve outcomes 3 . Measurement-based care is known to be effective, yet it is underused in practise because of the perceived burden of existing measurement tools 4 . For treatment purposes, automated assessment systems would have potential to help, if they could detect and measure depression with some reliability from easy-to-obtain material. Automated assessment systems may facilitate the detection and treatment of depression if they could reliably detect and measure depression in easy to obtain material.
Audio recording of speech is easy to obtain and may contain sufficient information for the detection and measurement of depression 5 , 6 , 7 . The potential vocal biomarkers for depression explored in previous works include a range of acoustic features, such as prosodic characteristics (e.g., pitch and speech rate), spectral characteristics (e.g., Mel-frequency cepstral coefficients and formant frequencies), and glottal (vocal fold) excitation patterns 8 , 9 , 10 , 11 . Previous work explored spectral, prosodic and glottal features for depression detection and severity estimation, but the accuracy and generalizability of depression detection is limited by the size of samples with available diagnostic information. Obtaining large samples of speech with diagnostic information is expensive and associated with ethical challenges of datasets combining identifiable (voice) and sensitive (diagnosis) information. One way of making better use of valuable datasets of limited size is to use models pre-trained on different but related tasks in much larger datasets.
Speech audio is routinely used for recognizing the identity of the speaker. Voice-based speaker identification is highly accurate thanks to models trained on large corpus; for instance the VoxCeleb2 12 dataset includes 3000 hours of speech by 7160 speakers. The experience of depression is intimately connected with the core of a person’s identity 13 . Depression is associated with self-focused attention and altered perception of the self 14 . The change between depressed and well states is so striking that recovery is commonly described as being a ’different person’. Based on the intimate link between depression and personal identity, we hypothesized that a model pre-trained for speaker identification will improve the detection of depression and estimation of depression severity from natural speech. In this work, we test this hypothesis by exploiting the representations of personal identity, known as speaker embeddings, in the detection and measurement of depression in speech.
To qualify the above hypothesis, we define speaker embeddings as text-independent speaker-specific information that include acoustic characteristics that are independent of what the speaker is saying. Speaker embeddings represent not only the identifiable information such as gender, age, etc., but have been shown to provide important cues about the traits of the speaker such as personality, physical state, likability, and pathology 15 . Speaker embeddings extracted from speech have previously been used for tasks such as automatic speaker verification 16 , improving speech recognition performance 17 , multi-speaker speech synthesis 18 , and emotion classification 19 . In this work, we apply speaker embeddings to the tasks of depression detection and severity estimation from speech. We empirically show that the speaker characteristics of an individual—as represented by speaker embeddings—are affected by changes in depression severity of the individual. We consider three established variants of speaker embeddings; the x-vectors, ECAPA-TDNN (Emphasized Channel Attention, Propagation, and Aggregation Time-delay neural network) x-vectors 20 , and d-vectors 21 . By using speaker embeddings, we demonstrate that large, public, unlabeled datasets in conjunction with much smaller labeled datasets, can be leveraged to improve on the state-of-the-art (SOTA) performance in clinically meaningful tasks with implications for public health.
Schematic depiction of the outline of the paper. There are three different phases in this work ( a ) Pre-training for speaker embeddings using a large non-medical speech data collected from N different speakers, ( b ) Depression analysis using speaker embeddings extracted from pre-trained models on longitudinal data, and ( c ) Depression detection and severity estimation using speaker embeddings extracted from pre-trained models.
The application of deep learning techniques significantly boosted the performance of depression detection using speech 22 , 23 , 24 , 25 , 26 , 27 . Initial work on speech-based depression detection used deep neural networks (DNNs) with fully-connected layers 22 . Then, convolutional neural networks (CNNs) and recurrent neural networks with long short-term memory (LSTM) units achieved better performance for depression detection and severity estimation 23 , 24 . Later, CNN-LSTM, dilated CNN and dilated CNN-LSTM models improved the SOTA performance in depression detection and severity estimation 25 , 26 , 27 , 28 . Further, sentiment and emotion embeddings were used for depression severity estimation 29 . To the best of our knowledge, none of the previous studies have explored the application of speaker embeddings for depression detection and severity estimation. i-vector-based models have been trained from scratch for detecting depression 30 , 31 , 32 , but these studies did not use i-vector models to extract speaker embeddings for depression detection. In this work, we use speaker embeddings to train multi-kernel CNN (MK-CNN) 33 and LSTM models for depression detection and severity estimation.
Our method consists of three phases, (1) Pre-training, (2) Depression analysis on longitudinal data, and (3) Depression detection and severity estimation. In pre-training phase of the speaker embedding models, given speech data collected from a large pool of speakers, we train speaker classification models to classify the speech samples based on the speaker labels. In the second phase, we use longitudinal data to analyze the effect of the changes in depression severity on speaker embeddings of an individual. In the third phase, we analyze the significance of speaker embeddings for the task of depression detection and severity estimation using speech. We use the speaker embeddings extracted using the pre-trained speaker classification models (trained in the first phase) in the second and third phases. Figure 1 shows an overview of our method.
In this work, we used two depression datasets, DAIC-WOZ 34 ((Distress Analysis Interview Corpus - Wizard of Oz—a corpus of clinical interviews) and Vocal Mind (spontaneous speech corpus obtained in a clinical setting) for analysis. DAIC-WOZ dataset contains a set of 219 clinical interviews collected from 219 participants (154 healthy and 65 depressed). Each audio sample was labeled with a PHQ-8 (Patient Health Questionnaire) score, in the range of 0–24, to denote the severity of depression. Vocal Mind dataset contains speech samples collected from 514 participants (403 healthy and 111 depressed). Depression severity of each speech sample was scored on the Montgomery and Asberg Depression Rating Scale (MADRS), which is in the range of 0–60. Further, longitudinal speech data also collected as a part of the Vocal Mind project was used. Longitudinal speech data was collected from 65 individuals at different dates, where variations in their depression severity scores were observed during this period. Manual transcripts with timestamps of the DAIC-WOZ and Vocal Mind datasets were used to discard the interviewer speech segments and retain only the participant speech segments for analysis. The retained participant speech segments were combined and were then divided into non-overlapping segments of 5–6 seconds in duration. This resulted in 15710 and 25144 segments for DAIC-WOZ and Vocal Mind datasets, respectively. The depression label assigned for each segment is same as the label of the entire speech sample. For DAIC-WOZ dataset, speech samples with PHQ-8 scores greater than or equal to 10 (PHQ-8 \(\ge \) 10) were considered as depressed and those samples with PHQ-8 scores less than 10 (PHQ-8 < 10) were considered as healthy. This corresponds to the recommended threshold for depression identification 35 , 36 . For the Vocal Mind dataset, speech samples with MADRS greater than or equal to 10 (MADRS \(\ge \) 10) were considered as depressed and those samples with MADRS less than 10 (MADRS < 10) were considered as healthy. This corresponds to the established threshold for remission on MADRS 37 . Table 1 provides various statistics of the DAIC-WOZ and the Vocal Mind datasets.
We use the pre-trained models available in speech-brain 38 for extracting the x-vectors and ECAPA-TDNN x-vectors from the speech samples. To extract d-vectors, we pre-trained the GE2E network on the task of speaker verification by consolidating two large non-clinical datasets (LibriSpeech 39 and VoxCeleb2 12 ), which are publicly available . LibriSpeech dataset consists of speech samples collected from 1166 speakers, and the VoxCeleb dataset consists of speech samples collected from 1166 speakers. In this work, We did not fine-tune the pre-trained speaker classification models on the depression datasets (i.e., DAIC-WOZ and Vocal Mind datasets) .
We then used these pre-trained models to extract speaker embeddings (x-vector, ECAPA-TDNN x-vectors, and d-vectors) at segment-level for the depression datasets. The dimensions of the speaker embeddings are 512, 256, and 192 for x-vector, ECAPA-TDNN x-vector, and d-vector, respectively. Finally, we use these speaker embeddings to train and test the LSTM and MK-CNN models for depression detection and severity estimation. We train separate models for x-vector, ECAPA-TDNN x-vector, and d-vector speaker embeddings.
We train MK-CNN (shown in Fig. 2 ) and LSTM networks with different speaker embeddings for depression detection and severity estimation.
We trained a MK-CNN model, as shown in Fig. 2 , for depression detection and severity estimation using the extracted speaker embeddings. The first convolutional layer consists of 3 different kernels with sizes (3, L ), (4, L ), and (5, L ), respectively. Here, L refers to the length of the input feature vector. L = 512, 256 and 192 for x-vector, ECAPA-TDNN x-vector and d-vector, respectively. Each kernel consists of 50 channels. In the second convolutional layer, the size of all kernels is 4, with 50 channels in each kernel. Outputs from each kernel of the second convolutional layer are flattened and then concatenated before passing through a fully-connected (FC) layer with 100 units and an output layer.
We also trained an LSTM network for depression detection and severity estimation using the extracted speaker embeddings. The LSTM network is the same as the MK-CNN network shown in Fig. 2 , with the MK-CNN block replaced by an LSTM block consisting of 2 LSTM layers with 128 units each. The output of the LSTM block, for the last timestep, is passed through an FC layer with 100 units and an output layer.
We considered a fully-connected deep neural network (DNN) as a baseline for comparison. This DNN has three hidden layers with 128, 64, and 128 ReLU units, respectively, followed by an output layer.
Further, we extracted COVAREP 24 and OpenSMILE 40 features for performance comparison with speaker embeddings. COVAREP and OpenSMILE features, obtained at the segment level, were used to train and test the MK-CNN, LSTM, and DNN networks. We extracted the 384-dimensional OpenSMILE features using the IS 09 configuration. We obtained the 444-dimensional COVAREP by computing the higher-order statistics (mean, maximum, minimum, standard deviation, skew, and kurtosis). We calculated statistics on the frame-level COVAREP features.
We also try combining speaker embeddings (one of the x-vector, ECAPA-TDNN x-vector or d-vector) with the OpenSMILE or COVAREP features (as shown in Fig. 3 ), for depression detection and severity estimation. The proposed network consists of two branches, one for speaker embeddings and the other for OpenSMILE or COVAREP features. The input features to each branch are passed through an LSTM (CE \(_{l}\) ) or MK-CNN (CE \(_{c}\) ) block and then through a fully-connected (FC) layer (100 units). The outputs of the FC layer of each branch are combined using dot product and then passed through an output layer to get the final decision.
For all the above networks, the final output layer is a softmax with two units when trained for the task of depression detection and a single linear unit when trained for depression severity estimation. The context in Figs. 2 and 3 refers to the number of contiguous segments in an audio recording considered to train and test the models. We experiment with temporal contexts of different lengths to analyze the optimal number of contiguous speech segments required to train the models (see subsection ”Temporal Context in Depression Detection” in supplementary material). Even though the networks are trained and tested at segment-level with different contexts, the final performance metrics are obtained based on the prediction for the entire audio file. For depression detection, we use majority voting on the segment-level decisions for the final decision. For depression severity score prediction, we compute the mean of the segment-level scores to compute the overall depression severity score.
Network for depression detection using speaker embeddings as input. S, C, K refers to the stride, number of channels and kernel size of the convolutional layer, respectively. FC refers to a fully-connected layer. The same network is used for OpenSMILE and COVAREP features.
Network for combining speaker embeddings, and OpenSMILE or COVAREP features for depression detection.
Here, we performed experiments on longitudinal speech data to analyze whether the speaker embeddings of an individual change as the depression severity score of that individual varies. For this analysis, we used the longitudinal data collected from speakers. For the given longitudinal speech samples, we extracted and analyzed different speaker embeddings i.e., x-vector, , ECAPA-TDNN x-vector, and d-vector. We then computed the cosine similarity scores between the speaker embeddings of the longitudinal speech samples. We also noted the difference in MADRS scores between the longitudinal samples. Finally, we analyzed the cosine similarity (A.B = ||A|| ||B|| cos \(\theta \) ) scores in relation to the variations in the MADRS score.
We used Adam optimizer ( \(\beta _1=0.9\) , \(\beta _2=0.99\) ), with an initial learning rate of 0.0005, to train all the networks. Dropout rates of 0.3, 0.4, and 0.3 were used for the MK-CNN block, LSTM block, and FC layers, respectively. ReLU activation was used for all the CNN, LSTM, and FC layers. All networks were trained for 50 epochs using a batch size of 128. For training the depression detection model, we used the negative log-likelihood loss function. Whereas for training the depression severity estimation model, we used the mean-squared error loss function. Class weights were set based on the distribution of samples in the train set to alleviate the class imbalance issue during training. We maintained a constant value for temporal context (number of contiguous segments in a sample) across the train, validation, and test phases.
Depression detection performance is measured using the \(F_1\) score ( \(F_1(D)\) and \(F_1(H)\) ) and balanced accuracy (BAc.). \(F_1(D)\) and \(F_1(H)\) are the \(F_1\) scores of depressed and healthy classes, respectively. Depression severity estimation performance is measured using root mean squared error (RMSE). The higher the \(F_1\) and BAc. values, the better the performance. Similarly, the lower the RMSE values, the better the performance. We report results using 5-fold cross-validation. There is no speaker overlap between folds, and we maintain the same proportion of depressed and healthy participants across all the folds.
Depression detection and severity estimation.
Tables 2 – 4 provide the experimental results obtained using ECAPA-TDNN x-vector (ECAPA) based speaker embeddings. Table 2 shows the depression detection and severity estimation performance when ECAPA speaker embeddings are combined with the OpenSMILE ((ECAPA, OpenSMILE)) or COVAREP ((ECAPA, COVAREP)) features, respectively. Models trained on speaker embeddings outperformed the models trained on COVAREP or OpenSMILE features for DAIC-WOZ and Vocal Mind datasets. The depression detection and severity estimation performance further improved when the speaker embeddings were used in conjunction with the OpenSMILE or COVAREP features. This shows that the speaker embeddings and the OpenSMILE or COVAREP features carry complementary information. The performance of the LSTM models was better or comparable to the MK-CNN models. To obtain the results in Tables 2 – 4 , we used a context of 16 segments for DAIC-WOZ, and a context of 20 segments for Vocal Mind datasets to train the LSTM and MK-CNN models. (see Supplementary Table S1 and S2 for the depression assessment results using x-vector and d-vector based speaker embeddings.)
We compared the performance of our proposed approach with previous SOTA approaches for depression detection and severity estimation (see Table 3 ). In Sequence 24 , LSTM models trained with COVAREP features were used for depression detection and severity estimation. In eGeMAPS 41 , CNN models were trained using OpenSMILE features for depression detection. In FVTC-MFCC 27 , channel-delayed correlations of MFCCs were used to train dilated CNN models. In FVTC-FMT 27 , channel-delayed correlations of formant frequencies were used to train dilated CNN models. None of these approaches explicitly considered speaker-specific features for depression detection. Table 3 shows that the models trained on speaker embeddings performed better (or at least comparable to) than the SOTA approaches for speech-based depression detection and severity estimation tasks. The depression detection and severity estimation performances obtained by combining speaker embeddings with the OpenSMILE features ((ECAPA, OS)) outperformed the previous SOTA approaches.
Analysis of speaker embeddings with respect to changes in depression severity scores using longitudinal data. ( a – c ) shows the variation in cosine similarity scores (between speaker embeddings extracted from longitudinal data) when the difference in MADRS score changes. ( d – e ) shows the variation in equal error rates (EER) (for the task of speaker classification) with respect to the difference in MADRS score between longitudinal samples. The different speaker embeddings are x-vector, d-vector and ECAPA-TDNN x-vector.
To understand the extent to which speaker embeddings make use of information beyond demographics such as biological sex and age for depression assessment, we trained machine learning models (decision trees, support vector machines and DNNs) for depression detection and severity estimation when only biological sex and age are provided as input. We found that the best performance obtained on the Vocal Mind dataset by combining biological sex and age ( \(F_1(D)\) = 0.16, \(F_1(H)\) = 0.65 and GM = 0.32, RMSE = 8.35) was significantly worse than the performance obtained by the speaker embedding ( \(F_1(D)\) = 0.34, \(F_1(H)\) = 0.81 and GM = 0.55, RMSE = 6.62). This shows that the speaker embeddings capture more information that is relevant for depression detection and severity estimation than just biological sex and age. Further details are provided in Supplementary Table S3 .
Previous works reported that some machine learning models simply learned gender-specific information from the voice for depression detection 42 , 43 , 44 . To analyze the contribution of the gender-agnostic information contained in speaker embeddings for depression detection, we performed gender-specific depression detection as done in previous works 43 , 44 . We observed from the experimental results that the speaker embeddings do not rely completely on the gender-specific information for depression detection. For the DAIC-WOZ dataset (see Supplementary Table S4 a), both Female and Male models achieved similar performance with the Female model performing slightly better than the Male model. Whereas for the Vocal Mind dataset (see Supplementary Table S4 b), there is a large difference between the performance of the Female and the Male models, with the Female model performing significantly better than the Male model. but could this also be partially explained if, e.g. males depression does not manifest as clearly in their voice? or maybe that is the point here? This might be attributed to the difference in imbalance ratio between non-depressed to depressed samples in each gender: for females, the imbalance ratio between non-depressed to depressed = 294:95 \(\approx \) 3:1 whereas for males the imbalance ratio between non-depressed to depressed = 109:16 \(\approx \) 7:1. Experimental results are provided in Supplementary Table S4 .
We compared the performance of the proposed speaker embeddings (d-vector and ECAPA-TDNN x-vectors) with embeddings extracted using other pre-training techniques such as Mockingjay 45 , vq-wav2vec 46 , wav2vec 2.0 47 , and TRILL 48 . We trained the MK-CNN and LSTM networks with the speech-based embeddings extracted from the different pre-trained models. In Table 4 , we reported results obtained using the LSTM networks (LSTM models performed better than the MK-CNN models across different embeddings). Speaker embeddings (both d-vector and ECAPA-TDNN x-vectors) performed better than the speech-based embeddings extracted using other pre-trained models. This signifies that the speaker embeddings alone could provide effective cues for detecting depression and estimating the severity of depression.
Figure 4 a–c shows the mean cosine similarity scores plotted with respect to the difference in MADRS scores between longitudinal speech samples. As the difference in the MADRS score increases, the cosine similarity value decreases. For longitudinal speech samples of a speaker, the higher the variation in MADRS score, the higher the variation in speaker embeddings for that speaker.
Figure 4 d–f shows the mean equal error rates (EER in %) plotted with respect to the difference in MADRS scores between longitudinal speech samples. As the difference in the MADRS score increases, the EER values increases. This further confirms that for longitudinal speech samples of a speaker, the higher the variation in MADRS score, the higher the variation in speaker embeddings of that speaker.
It can also be observed that the variance or EER in speaker embeddings increase as the difference in depression severity scores increase. One reason for this behavior could be the skewed distribution of the samples across different values. There are more longitudinal samples with low differences in depression severity compared to samples with higher differences in depression severity. This might have led to higher variance at the end of the curve. Higher number of longitudinal samples might give us a better understanding of this behavior.
We also analyzed the effectiveness of the extracted speaker embeddings (d-vector and ECAPA-TDNN x-vectors) for the task of speaker classification. The DAIC-WOZ dataset consists of recordings from 189-speakers—189-class speaker classification. Similarly, the Vocal Mind dataset consists of recordings from 514-speakers — 514-class speaker classification. We randomly selected 25 and 15 non-overlapping segments from each speaker to form the train and test sets for that speaker. We extracted ECAPA-TDNN x-vectors and d-vectors for all the samples. We trained logistic regression classifiers (with no hidden layers) separately on the d-vectors and ECAPA-TDNN x-vectors for the task of speaker classification. Speaker classification results are reported in terms of equal error rate (EER)—lower the value of EER, better the performance. Using d-vectors, we achieved EERs of 1.29 and 1.69 on the test sets of DAIC-WOZ and Vocal Mind datasets, respectively. Using ECAPA-TDNN x-vectors, we achieved EER values of 1.10 and 1.46 on the test sets of DAIC-WOZ and Vocal Mind datasets, respectively. These low EER values show that the extracted speaker embeddings carry crucial information about the speaker-specific characteristics.
To provide context for interpreting the lower RMSE values achieved by our proposed depression assessment system (i.e. an LSTM model trained by combining ECAPA-TDNN speaker embeddings with OpenSMILE features), we present a detailed confusion matrix (see Fig. 5 ): We used known levels of depressive severity to evaluate the seriousness of misclassification. We found that our ECAPA-TDNN-Open SMILE model made the less severe mistakes of misclassifying between healthy controls and mild cases of depression, as shown in Fig. 5 a. This compares favourably to the no-information system that is equally likely to make the bigger mistake of misclassifying severe cases of depression as controls (see Fig. 5 b).
Specifically, the depression severity score values (PHQ-8) are clinically divided into 4 different groups: No depression or healthy (PHQ-8<= 8), Mild depression (PHQ-8 range 9-12), Moderate depression (PHQ-8 range 13-16) and Severe depression (PHQ-8 range 17-24). In matrix (a) on the left, we show a confusion matrix based on our system’s predicted regression scores and in matrix (b) we show a confusion matrix obtained for a Majority classifier (or a no-information system). These matrices demonstrate interesting characteristics: (1) Many of the errors made by our model are between healthy (None) and mild classes, which would likely be more tolerable, since a goal would be to track longitudinal changes; if a patient is already known to be depressed, then it may be less critical for a system to automatically detect where they lie relative to this particular border. (2) Our system misclassified only 5 patients who are clinically depressed as healthy (None), and 4 of these are mild depression cases. This is a less significant error than it would be to misclassify a severely depressed patient as being healthy (i.e. failing to flag them). The no-information system (majority predictor) classified all 16 clinically depressed patients as healthy. Indeed it would always have all of its errors in the first column: misclassifying all depressed patients as being healthy, regardless of the severity of their depression. (3) Indeed, in our system, none of the severely depressed patients are misclassified as healthy, whereas in the no-information system, 100% of severely depressed patients will be misclassified as healthy (red bin in Fig. 5 b) (4) For our proposed system, most of the misclassification errors are “one bin apart” (light green diagonals in Fig. 5 a), i.e. confusion between adjacent classes such as mild-none or mild-moderate, as opposed to confusion between more separated classes such as none-moderate. The no-information system misclassified all the 3 moderately depressed people as healthy and the 4 severely depressed people as healthy.
Confusion matrix obtained by considering predicted depression severity scores (PHQ-8) by ( a ) our proposed system—LSTM model trained combining ECAPA-TDNN with OpenSMILE features, and ( b ) a no-information system which predicts the mean value for every input. Fine grained clinical levels of the predicted depression severity scores obtained by dividing the depression severity scores into 4 different groups: None (PHQ-8<= 8); Mild (PHQ-8 range 9–12), Moderate (PHQ-8 range 13–16) and Severe (PHQ-8 range 17–24).
In this work, we showed that speaker embeddings can be used to build machine learning models for depression assessment. Using speaker embeddings in combination with acoustic features, we achieved incremental progress in performance over the previous state-of-the-art machine learning techniques for the tasks of depression severity estimation and depression detection. However, there is a need to further improve performance before deploying AI-based depression assessment systems. In this work, we considered acoustic features, but not text-based features (i.e. linguistic content). It is possible that the latter, in combination with acoustic features, might in future further improve the performance of these machine learning models. The main objective of this work is not to build machine learning models to replace human clinicians, but to develop models which can be used for measurement-based treatment and to assist (i.e. work in co-ordination with) human clinicians in making better assessment of depression. Moreover, the specificity of the current models in diagnosing depression from other mental disorders remains to be established.
In this work we train a speaker embedding network on standard large datasets and then use two small clinical datasets to show that the resulting embeddings can then be used to estimate the severity of depression and to detect depression from speech. In particular, when we combine these embeddings with OpenSMILE speech features, we achieve SOTA performance on the depression severity estimation and the depression detection tasks. Further, we show that the changes in depression severity affects the speaker identification by analyzing repeated speech samples collected from a subset of speakers.
Publicly available Voxceleb2 ( https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox2.html ) and LibriSpeech ( https://www.openslr.org/12 ) datasets were used to train the speaker embedding models i.e., x-vector, d-vector and ECAPE-TDNN x-vector models. The DAIC-WOZ dataset is publicly available at https://dcapswoz.ict.usc.edu/ ). The Vocal Mind dataset generated and analyzed during the current study is not publicly available due to potential identifiable character of speech data, sensitive character of the associated information on mental disorders, and limits of consent provided by participants. The study procedures for Vocal Mind dataset, and all the experiments in this research have been carried out in accordance with the Canadian Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans - TCPS 2 (2018) policy statement. The Research Ethics Board of Nova Scotia Health Authority approved all study procedures. All the participants provided written informed consent. The consent covers the publication of de-identified data and results. The consent does not permit publication of identifiable information. A proportion of participants have additionally consented for their de-identified audio recordings to be shared with other researchers in other Canadian research institutions and/or research institution outside of Canada. De-identified version of these samples are available from the corresponding author on reasonable request.
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This work has been supported by the Canada Research Chairs Program (File Number 950 - 233141) and the Canadian Institutes of Health Research (Funding Reference Number 165835). We thank the Canadian Institute for Advanced Research (CIFAR) for their support. Resources used in preparing this research were provided, in part, by NSERC, the Province of Ontario, the Government of Canada through CIFAR, and companies sponsoring the Vector Institute www.vectorinstitute.ai/#partners .
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S.H.D. designed and conducted the experiments, and wrote the first draft of the paper. S.R. helped in conducting experiments and plotting the figures. K.D., R.L. and S.R. designed the data collection process, and collected and annotated the data. R.U. and S.O. were involved in the discussions of the approach, and provided critical feedback to the paper. All authors have discussed the results and reviewed the manuscript.
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Dumpala, S.H., Dikaios, K., Rodriguez, S. et al. Manifestation of depression in speech overlaps with characteristics used to represent and recognize speaker identity. Sci Rep 13 , 11155 (2023). https://doi.org/10.1038/s41598-023-35184-7
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64 Pages Posted: 25 Aug 2024
South China Normal University
Xiaolin guo, yaling wang, jiaxuan liu, daniel kaiser.
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Shenzhen University
Inner speech, a silent verbal experience, is central to human consciousness and cognition, yet its neural mechanisms remain largely unknown.In this study, we adopted an ecological paradigm called situationally simulated inner speech, which involves the dynamic integration of contextual background, episodic and semantic memories, and external events into a coherent structure. We conducted dynamic activation and network analyses on fMRI data as participants engaged in inner speech prompted by cue words across ten contexts. Our seed-based co-activation pattern analyses revealed dynamic involvement of the language network, sensorimotor network, and default mode network in situationally simulated inner speech. Additionally, frame-wise dynamic conditional correlation analysis uncovered four temporal-reoccurring states with distinct functional connectivity patterns among these networks. We proposed a triple network model for deliberate inner speech, including language network for truncated overt speech, sensorimotor network for perceptual simulation and monitoring, and default model network for integration and 'sense-making' processing.
Note: Funding declaration: This work was supported by the National Social Science Foundation of China (No. 20&ZD296), KeyArea Research and Development Program of Guangdong Province (No.2019B030335001), National Natural Science Foundation of China (No.32100889). The funding agencies took no part in the design or implementation of the research. D.K. is supported by the Deutsche Forschungsgemeinschaft (SFB/TRR135, project number 222641018; KA4683/5-1, project number 518483074; KA4683/6-1, project number 536053998), “The Adaptive Mind” funded by the Excellence Program of the Hessian Ministry of Higher Education, Science, Research and Art, and a European Research Council starting grant (ERC-2022-STG 101076057). Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. Conflict of Interests: The authors declare no competing financial interests.
Keywords: coactivation pattern, functional magnetic resonance imaging, situationally simulated inner speech, triple network model
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Abstract: How do you know when a scoping review is a good fit for your literature review? As an undergraduate student, I participated in a project where our goal was to analyze commonly employed methodologies used to assess gender perception in speech, demographic characteristics of listeners that have been recorded, and the types of speech samples being utilized to investigate gender perception for speech pathology. However, there are many literature review styles to choose from before moving forward with a project or idea. We found that a scoping review would be the most appropriate tool to meet our goals because it highlights literature in emerging areas of science that have not been reviewed. A scoping review assesses the potential scope of research done about a certain topic in hopes of retrieving evidence on the team's research topics. To conduct our scoping review, we used the software, Covidence, which allows reviewers to complete article screening and data extraction quickly and flexibly. Articles went through multiple stages, abstract and title screening, full-text screening, and data extraction, to be filtered and eventually included in our findings. Through this experience in reviewing literature, I have gained knowledge on the benefits and disadvantages of scoping reviews, how to navigate through research article sections, and how to create a thought pattern that seeks out information for future research questions related to health disparities.
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The exponential growth of social media has brought with it an increasing propagation of hate speech and hate based propaganda. Hate speech is commonly defined as any communication that disparages a person or a group on the basis of some characteristics such as race, colour, ethnicity, gender, sexual orientation, nationality, religion. Online hate diffusion has now developed into a serious problem and this has led to a number of international initiatives being proposed, aimed at qualifying the problem and developing effective counter-measures. The aim of this paper is to analyse the knowledge structure of hate speech literature and the evolution of related topics. We apply co-word analysis methods to identify different topics treated in the field. The analysed database was downloaded from Scopus, focusing on a number of publications during the last thirty years. Topic and network analyses of literature showed that the main research topics can be divided into three areas: “general debate hate speech versus freedom of expression”,“hate-speech automatic detection and classification by machine-learning strategies”, and “gendered hate speech and cyberbullying”. The understanding of how research fronts interact led to stress the relevance of machine learning approaches to correctly assess hatred forms of online speech.
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In recent years, the ways in which people receive news, and communicate with one another, have been revolutionised by the Internet, and especially by social networks. It is a natural activity, in societies where freedom of speech is recognised, for people to express their opinions. From an era in which individuals communicated their ideas, usually orally and only to small numbers of other people, we have moved on to an era in which individuals can make free use of a variety of diffusion channels in order to communicate, instantaneously, with people who are a long distance away; in addition, more and more people make use of online platforms not only to interact with each other, but also to share news. The detachment created by being enabled to write, without any obligation to reveal oneself directly, means that this new medium of virtual communication allows people to feel greater freedom in the way they express themselves. Unfortunately, though, there is also a dark side to this system. Social media have become a fertile ground for heated discussions which frequently result in the use of insulting and offensive language. The creation and dissemination of hateful speech are now pervading the online platforms. As a result, countries are recognising hate speech as a serious problem, and this has led to a number of International and European initiatives being proposed, aimed at qualifying the problem and developing effective counter-measures.
A first issue, for the identification of a content as hateful, is that there is no universally accepted definition of hate speech, mainly because of the vague and subjective determinations as to whether speech is “offensive” or conveys “hate” (Strossen 2016 ). A comprehensive overview of different definitions can be found in Sellars ( 2016 ) who derives several related concepts that appear throughout academic and legal attempts to define hate speech as well as in attempts of online platforms. The identified common traits refer to: the targeting of a group, or an individual as a member of a group; the presence of a content that expresses hatred, causes a harm, incites bad actions beyond the speech itself, and has no redeeming purpose; the intention of harm or bad activity; the public nature of the speech; finally, a context that makes violent response possible. Sellars ( 2016 ) stresses, however, how the identified traits do not form a single definition, but could be used to help improve the confidence that the speech in question is worthy of identification as hate speech.
In addition to the ambiguity in the definition, hate speech creates a conflict between some people’s speech rights, and other people’s right to be free from verbal abuse (Greene and Simpson 2017 ). The complex balancing between freedom of expression and the defence of human dignity has received significant attention from legal scholars and philosophers and, according to Sellars ( 2016 ), the different approaches to define hate speech can be linked to academics’ particular motivations: “Some do not overtly call for legal sanction for such speech and seek merely to understand the phenomenon; some do seek to make the speech illegal, and are trying to guide legislators and courts to effective statutory language; some are in between.” Advocates of the free speech rights invoke the principle of viewpoint neutrality or content neutrality, which prohibits bans on the expression of viewpoints based on their substantive message (Brettschneider 2013 ). This protection extends even to speech that expresses ideas that most people would find distasteful, offensive, disagreeable, or discomforting, and thus extends even to hate speech (Beausoleil 2019 ). According to Strossen ( 2016 , 2018 ) hate speech laws not only violate the cardinal viewpoint neutrality, but also the emergency principles, by permitting government to suppress speech solely because its message is disfavoured, disturbing, or feared to be dangerous, by government officials or community members, and not because it directly causes imminent serious harm. On the other hand, Cohen-Almagor ( 2016 , 2019 ) insists that it is necessary to “take the evils of hate speech seriously” and that “certain kinds of speech are beyond tolerance.” The author criticizes the viewpoint neutrality concept arguing that a balance needs to be struck between competing social interests because freedom of expression is important as is the protection of vulnerable minorities: “people must enjoy absolute freedom to advocate and debate ideas, but this is so long as they refrain from abusing this freedom to attack the rights of others or their status in society as human beings and equal members of the community.” An alternative remedy to censoring hate speech could be to add more speech, as suggested by the UNESCO study titled “Countering On-line Hate Speech” (Gagliardone et al. 2015) which argues that counter-speech is usually preferable to the suppression of hate speech.
The rising visibility of hate speech on online social platform has resulted in a continuously growing rate of published research into different areas of hate speech. The increasing number of studies on this subject is beneficial to scholars and practitioners, but it also brings about challenges in terms of understanding the key research streams in the area. Previous surveys highlighted the state of the art and the evolution of research on hate speech (Schmidt and Wiegand 2017 ; Fortuna and Nunes 2018 ; MacAvaney et al. 2019 ; Waqas et al. 2019 ). The survey of Schmidt and Wiegand ( 2017 ) describes the key areas that have been explored to automatically recognize hateful utterances using neural language processing. Eight categories of features used in hate speech detection, including simple surface, word generalization, sentiment analysis, lexical resources and linguistic characteristics, knowledge-based features, meta-information, and multimodal information, have been highlighted. In addition, Schmidt and Wiegand ( 2017 ) stress how a comparability of different features and methods requires a benchmark data set. Fortuna and Nunes ( 2018 ) carried out an in-depth survey aimed at providing a systematic overview of studies in the field. In this survey, the authors firstly pay attention to the motivations for studying hate speech and then they conveniently distinguish theoretical and practical aspects. Specifically, they list some of the main rules for hate speech identification and investigate the methods and algorithms adopted in literature for automatic hate speech detection. Also, practical resources, such as datasets and other projects, have been reviewed. MacAvaney et al. ( 2019 ) discussed the challenges faced by online automatic approaches for hate speech detection in text, including competing definitions, dataset availability and construction. A throughout bibliographic and visualization analysis of the scientific literature related to online hate speech was conducted Waqas et al. ( 2019 ). Drawing on Web of Science (WOS) core database, their study concentrated on the mapping of general research indices, prevalent themes of research, research hotspots and influential stakeholders, such as organizations and contributing regions. Along with the most popular bibliometric measures, such as total number of papers, to measure productivity, and total citations, to assess the relevance of a country, institution, or author, the above mentioned research uses mapping knowledge tools to draw the structure and networks of authors, journals, universities and countries. Not surprisingly, the results of this bibliometric analysis show a remarkable increase in publication and citation trend after year 2005, when social media platforms have grown in terms of influence and user adoption, and the Internet has become a central arena for public and private discourse. Furthermore, it has emerged that most of the publications originate from the discipline of psychology and psychiatry, with recurring themes of cyberbullying, psychiatric morbidity, and psychological profiling of aggressors and victims. As noted by the authors, the high representation of psychology-related contributions is mainly due to the choice of WOS core database, which excludes relevant research fields from the analysis, being its coverage geared towards health and social science disciplines rather than engineering or computer ones.
Based on these previous studies, and especially on that of Waqas et al. ( 2019 ), our research intends to enlarge the mapping of global literature output regarding online hate speech over the last thirty years, by relying on bibliographic data extracted from Scopus database and using different methodological approaches. In order to identify how online hate scientific literature is evolving and understand what are the main research areas and fronts and how they interact over time, we used bibliometric measures, mapping knowledge tools and topic modelling. All the above methods are traditionally employed in bibliometrics analysis and share the idea of using a great amount of bibliographic data to let emerge, in an unsupervised way, the underlying knowledge base. In particular, topic analysis, based on the Latent Dirichlet Allocation method (LDA; Blei et al. 2003 ) is gaining popularity among scholars in diverse fields (Alghamdi and Alfalqi 2015 ). A topic model leads to two key outputs: a list of topics (i.e. groups of words that frequently occur together) and a lists of documents that are strongly associated with each topic (McPhee et al. 2017 ). Accordingly, this approach is useful for finding interpretable topics with semantic meaning and for assigning these topics to the literature documents, offering in such way a probabilistic quantification of relevance both for the identification of topics and for the classification of documents.
Our study exploits the main strengths of each method in drawing a synthetic representation of the research trends on online hate and adds value to previous quoted works, by taking advantage of topic modelling to retrieve latent driven themes. As highlighted in Suominen and Toivanen ( 2016 ), the key novelty of topic modelling, in classifying scientific knowledge, is that it virtually eliminates the need to fit new-to-the-world knowledge into known-to-the-world definitions.
The remainder of this work is structured as follows. Section “Materials and methods” describes the data source and the methods used. Section “Results” presents the bibliometric results, focusing on the yearly quantitative distribution of publications and on the latent topics retrieved through LDA. This section provides useful insights into the temporal evolution of the topics, their interactions and the research activity in the identified latent themes. A conclusion and future perspectives are given in “Conclusion” section. Finally, we report additional information on the bibliographic data set and the topic analysis results, in the online Supplementary Material.
Bibliographic dataset.
For the analysis, we use a bibliometric dataset, covering the period 1992–2019, retrieved from Scopus database. This bibliographic database was selected because it is one of the most suitable source of references for scientific peer-reviewed publications.
In the same vein of Waqas et al. ( 2019 ), we focus on online hate and, for our search, we built a query that, in addition to the exact phrase “hate speech”, combines terms related to offensive or denigratory language (“hatred”, “abusive language, “abusive discourse”, “abusive speech”, “offensive language”, “offensive discourse”, “offensive speech”, “denigratory language”, “denigratory discourse”, “denigratory speech”) with words linked to the online nature (“online”,“social media”, “web”, “virtual”, “cyber”, “Orkut”, “Twitter”, “Facebook”, “Reddit”, “Instagram”, “Snapchat”, “Youtube”, “Whatsapp”, “Wechat”, “QQ”, “Tumblr”, “Linkedin”, “Pinterest”).
We have not considered specific terms linked to cyberbullying because, although if this phenomenon overlaps partially with hate speech, it encompasses a broader field. The exact query can be found in the Supplementary Material.
The bibliographic data was extracted by applying the query to the contents of title, abstract and keywords. The data for each resulting publication was manually exported on December 15, 2019.
All types of publications were included in the search, and 1614 documents related to hate speech, published in 995 different sources, were identified. This high number indicates a wide variety of research themes, and the multidisciplinary character of the subject which involves a plurality of disciplines. In particular, the top publication fields include Social Sciences, Computer Science, Arts and Humanities and Psychology. Looking at the document type, the majority is article, conference paper and book chapter.
Information about document distribution by research field is given in the Supplementary Material, along with the document distribution by source and the ranking of the most productive countries and authors.
To investigate the structure of research on hate speech, we firstly consider an exploratory analysis of the keywords selected by the authors. The analysis was carried out through the R package Bibliometrix (Aria and Cuccurullo 2017 ), which allows to perform multiple correspondence analysis (MCA) (Greenacre and Blasius 2006 ) and hierarchical clustering to draw a conceptual structure map of the field. Specifically, MCA allows to obtain a low-dimensional Euclidean representation of the original data matrix, by performing a homogeneity analysis of the “documents by keywords” indicator matrix, built by considering a dummy variable for each keyword. The words are plotted onto a two-dimensional map where closer words are more similar in distribution across the documents. In addition, the implementation of a hierarchical clustering procedure on this reduced space leads to identify clusters of documents that are characterised by common keywords.
To gain a deeper understanding of the topics discussed in the published research on hate speech, we have applied Latent Dirichet Allocation, which is an automatic topic mining technique that enables to uncover hidden thematic subjects in document collections by revealing recurring clusters of co-occurring words. The two foundational probabilistic topic models are the Probabilistic Latent Semantic Analysis (pLSA, Hofmann 1999 ) and the Latent Dirichlet Allocation (Blei et al. 2003 ). The pLSA is a probabilistic variant of the Latent Semantic Analysis introduced by Deerwester et al. ( 1990 ) to capture the semantic information embedded in large textual corpora without human supervision. In the pLSA approach, each word in a document is modelled as a sample from a mixture model, where the mixture components are multinomial random variables that can be viewed as representations of topics. The pLSA model allows multiple topics in each document, and the possible topic proportions are learned from the document collection. Blei et al. ( 2003 ) introduced the LDA which presents a higher modelling flexibility over pLSA by assuming fully complete probabilistic generative model where each document is represented as a random mixture over latent topics and each topic is characterized by a distribution over words. LDA mitigates some shortcomings of the earlier topic models. Specifically, it has the advantage to improve the way of mixture models of capturing the exchangeability of both words and documents. LDA assumes a probabilistic generative model where each document is described by a distribution of topics and each topic is described by a distribution of words. The set of candidate topics are the same for all documents and each document may contain words from multiple different topics. The generative two-stage process of each document in the corpus can be described as follows (Blei 2012 ). In the first step a distribution over topics is randomly chosen; in the second step for each word in the document a topic is randomly chosen from the distribution over topics and a word is randomly chosen from the corresponding distribution over the vocabulary. Following Blei ( 2012 ), it it is possible to describe LDA more formally. Let assume that we have a corpus defined as a collection of D documents where each document is a sequence of N words, \(w_d=(w_{d,1},w_{d,2},\dots ,w_{d,N})\) , and each word is an item from a vocabulary indexed by \(\{1,\dots ,V\}\) . Furthermore, we assume that there are K latent topics, \(\beta _{1:K}\) , defined as distribution over the vocabulary. The generative process for LDA corresponds to the following joint distribution of the hidden and observed variables
The topic proportions for the d th document are \(\theta _d\) , where \(\theta _{d,k}\) is the topic proportion for topic k in document d . The topic assignments for the d th document are \(z_d\) , where \(z_{d,n}\) is the topic assignment for the n th word in document d . Both the topic proportions and the topic distributions over the vocabulary follow a Dirichlet distribution. Since the posterior distribution, \(p \left( \beta _{1:K},\theta _{1:D},z_{1:D}|w_{1:D}\right) \) , is intractable for exact inference, a wide variety of approximate inference algorithms, such as sampling-based (Steyvers and Griffiths 2006 ) and variational (Blei et al. 2003 ) algorithms can be considered.
In our analysis, we implement LDA to model a corpus where each document consists of the publication title, its abstract and the keywords. To exctract the relevant content and remove any unwanted nuisance terms, we performed a cleaning process (tokenization; lowercase conversion; special characters, and stop-words removal) of the text documents using the function provided in the Text Analytics Toolbox of Matlab (MATLAB 2018 ). For the analyses, the tokens with less than 10 occurrences in the corpus have been pruned. LDA analysis was performed through the fitlda Matlab routine available in the same Toolbox.
The results of this study involved different analyses. Firstly, we concentrated on the yearly quantitative distribution of literature, then we examined the conceptual structure of hate speech research. Next, we combined the results of topic and network analysis for highlighting the emerging topics, their interactions over time, the most influential countries and the academic cooperations in the retrieved themes.
The evolution over time of the number of published documents shows a remarkable growth, highlighting the increased global focus on online hate. See Fig. 1 , in which the number of publications per year is displayed.
Since 1992, it is possible to distinguish between two different phases. During the first phase from 1992 to 2010, a slow increase in publications occurred. A higher growth rate characterises, instead, the second phase, from 2010 to 2019, testifying the growing interest. This is consistent with Price’s theory on the productivity on a given subject (Price 1963 ), according to which the development of science goes through three phases. In the preliminary phase, known as the precursor, when some scholars start publishing research into a new field, small increments in scientific literature are recorded. In the second phase, the number of publications grows exponentially, since the expansion of the field attracts an increasing number of scientists, as many aspects of the subject still have to be explored. Finally, in the third phase there is a consolidation of the body of knowledge along with a stabilisation in the productivity; therefore the aspect of the curve transforms from exponential to logistic.
To verify the rapid increase in the trend of research literature related to online hate speech, we fit an exponential growth curve to the data (Price 1963 ). According to this model the annual rate of change is equal to \(20.5\%\) . Therefore, it can be said that hate speech research is in the second phase of development: an increasing amount of research is being published, but there is still room for improvement in many aspects.
Number of publications on hate speech per year: observed and expected distribution according to an exponential growth
The conceptual structure of the research on hate speech is represented in Fig. 2 , where authors’ keywords, whose occurrences are greater than ten, are represented on the two dimensional plane obtained through Multiple Correspondence Analysis (MCA).
Conceptual map of hate speech research
The two dimensions of the maps which emerged from the MCA can be interpreted as follows. The first, horizontal, dimension separates keywords emphasizing social networks and communities and hate speech linked to religion (on the right), from those related to the political aspects of the hate speech phenomenon (on the left). This dimension explains the \(39.61\%\) of variability. The second, vertical dimension, considers machine learning techniques and accounts for the \(13.55\%\) of overall inertia. In Fig. 2 are also displayed the results obtained through a hierarchical cluster analysis carried out adopting the method of the average linkage on the factorial coordinates obtained with the MCA. A very important fact is evident from the conceptual map: three clusters represent the three major areas of research involved in the matter of hate diffusion. The blue cluster shows words as “abusive language”, “cyberbullying”, “deep learning”, “text classification”, “sentiment analysis”, “social network”, terms that bring out the problem related to automatic detection. The green cluster shows words as “human rights”, “democracy”, “incitement”, “blasphemy”, words that bring out the problem related to the legal sphere. The red cluster, the most numerous, shows words as “social network analysis”, “privacy”, “youtube”, “facebook”, “online hate”, “cyberhate”, words that bring out the problem related to social sphere and social media.
Topic modelling, performed via LDA technique, provides an additional insight in structuring the online hate research into different topics. As known, LDA algorithm needs to specify a fixed number of topics, implying that the researchers should have some idea of the possible bounds of latent features in the text. In fact, there is no unique value, appropriate in all situations and all datasets (Barua et al. 2014 ). Of course, the LDA model produces finer-grained aggregations by increasing the number of desired topics while smaller values will produce coarser-grained, more general topics. On the other hand, a higher number of topics may cause the progressive intrusion of non-relevant terms among the most probable words, affecting the semantic coherence of the retrieved themes.
In our study, we run the LDA analysis by setting the number of desired topics, in turn, equal to 10, 12, 14 and, in the end, we adopted the twelve-topic solution which guarantees a fair compromise between topic interpretability and a detailed analysis.
In LDA, the topics are assumed to be latent variables, which need to be meaningfully interpreted. This is usually achieved by examining the top keywords in each topic (Steyvers and Griffiths 2006 ). Figures 3 and 4 show the most relevant words for each topic, where relevance is measured normalizing the posterior word probabilities per topic by the geometric mean of the posterior probabilities for the word across all topics. Topics are sorted according to the estimated probability to be observed in the entire data set. The most relevant terms, along with their relevance measures are provided in Section 2.1 of the Supplementary Material.
The twelve identified topics reveal important areas of online hate research in the past thirty years. They can be synthetically described as dealing with the following themes.
Word clouds for topics 1–6
Word clouds for topics 7–12
Topic 1 includes words such as “speech”, “hate”, “free”, “harm”, “freedom”, suggesting a broad discussion on the debate “hate speech” versus “free speech”. The constitutional right of freedom of expression is considered also in Topic 3, mainly characterised by words like “freedom”, “law/laws”, “rights”, “expression”,“constitutional”. Topic 2 is strictly linked with the political aspects of the hate speech phenomenon and contains terms such as “political/politics/politician”, “discourse”, “democracy”, “elections”. Topic 7 covers hate speech related to religion and extremism and is described by words such as “terrorism/terrorist”, “religion/religious”, “muslim/muslims”, “violence”, “global”,“war”, “extremism/extremist”.
The online aspect of hate is clearly highlighted in Topics 4, 6, 8 and 10. In particular, Topic 4 is related to research on social networks and communities, especially Facebook and Youtube, which are large social media providers whose inner mechanisms allow users to report hate speech. Studies in Topic 8 refer to Twitter, and it is possible to stress how they make use, above all, of content and sentiment analysis. Topic 6 covers the aspect of information diffusion on the Internet, including terms like “internet”, “information”, “media”. Finally, Topic 10 considers the problem of online deviant behaviour and cyberbullying, in which relevant words are: “online”, “exposure”, “crime/crimes”, “behavior”, “cyberbullying”, “cyberhate”.
Interestingly, the distinct hate speech targets are disclosed by Topics 5 and 11. Topic 5 deals with issues on racism, as indicated by the following sets of words: “racism”, “racist”, “race”, “racial”,“white/whiteness”, “black’; in that topic we also find, among the top scoring words, some terms associated with feminism (i.e.“feminist”, “women”, “misogyny”). Topic 11 refers to hate speech linked to gender and sexual identity since the most relevant-used words are: “sexual/sexuality”, “gender”, “gay”, “trasgender”, “lesbian”, “lgbt/lgbtq”.
Finally, Topics 9 and 12 deals with methodological aspects of hate speech analysis. In particular, Topic 9 refers to the analysis of discourse and language, as suggested by the most relevant words contained in it (“comments”, “discourse”, “language”, “emotions”, “linguistic”,“corpus”). On the other hand, Topic 12 considers machine learning techniques, in fact, within this specific topic, the terms “learning”, “detection”, “classification”, “machine”,“text” are those with the top scoring.
To further analyse each of the topics, we focus on their dynamic changes over the years. As previously pointed out, LDA algorithm estimates each topic as a mixture of words, but also models each document as a mixture of topics. Therefore, each document can exhibit multiple topics on the base of the words used. The estimated probabilities of observing each topic in each document can be exploited to assign one or more topics to the documents of the analysed bibliographic dataset. Specifically, in this study, we decided to assign the topics with the top three highest document-topic probabilities to each document, provided the probabilities are greater than 0.2.
The temporal evolution of the scientific productivity for each topic can be captured through Fig. 5 , where the exponential growth model has been fitted considering the number of documents published since 2000.
Number of publications for each topic: observed and expected distributions according to an exponential growth
The temporal trend of most topics agrees with an exponential growth. However, looking at Topic 1 and Topic 3, we notice how the number of publications in the last period falls below the number expected according to the exponential law considered by Price ( 1963 ) with regard to the second phase in the development of scientific research on a given subject. We saw that the content of Topics 1 and 3 is associated with generic themes of online hate speech, thus the lesser amount of related publications in the last period reflects the interest of research community in identifying new research fronts. Conversely, the number of published documents for Topic 8 shows a sudden rise starting from 2018. This conclusion holds, even if to a lesser extend, for Topic 9 where the observed productivity rises above the expected one.
The notable case in Fig. 5 regards Topic 12, dealing with the application of the dominant and new theme of machine learning algorithms to online hate speech. In the last two years, this topic exhibits an explosive growth as for the related publication volumes. A relatively more contained rise in the size of publications is recorded for Topics 10 and 11, whose contents are associated with the specific themes of cyberhate and gendered hate.
Overall, these temporal patterns seem to suggest a shift in hate speech literature from more generic themes, about the debate on freedom of speech versus hate speech, towards research more focused on the technical aspects of hate speech detection and methodologies and techniques included in the fields of linguistics, statistics and machine learning. The appearance and development of new fields of interest and innovative ideas in the research activity on hate speech is confirmed by the heatmaps provided in the Supplementary Material, which show the number of documents, by years, assigned to the identified topics.
After exploring the features of the identified topics in online hate speech research, we quantitatively model their interactions and build a topic relation network. In particular, given that each document has been assigned to multiple topics, we can exploit the topic co-occurence matrix in order to understand the connections among the different themes developed in this field of research.
In Fig. 6 , we display the topic network. In the graph, the nodes are coloured according to their degree and the edges are weighted according to the co-occurences: the wider the line, the stronger the connection. Moreover, the edges whose weight is lower than the average co-occurence number have been removed. Details on the connections are provided in Section 2.3 of the Supplementary Material.
Topic co-occurence network for the publication on hate speech from 1992 to 2019
From the analysis of the links it is possible disclosing interesting relations between research fronts, which underline the multi-disciplinary nature of online hate research and the crossbreeding between different disciplines and research subjects. The strongest connection is between Topics 1 and 3, dealing respectively with the broad debate of hate speech versus free speech and the constitutional right of freedom of expression, respectively. This relation reflects the fact that both the topics are related to the boundaries of freedom of expression; accordingly, it is obvious to observe an overlapping of these two themes among documents. Through the network visualization, we see that Topic 1, being a general theme, is connected with the majority of the nodes. Other most connecting nodes are referred to the topic dealing with the questions of free speech (Topic3 ) and to the activities of hateful users on online social media (Topic 4). An interesting clique shows how closely connected are also Topics 4, 8 and 12. The interactions of this subgroup of nodes reveal the relation between computer sciences and social sciences disciplines.
The importance of the retrieved topics in the network of connections can be inferred considering the degree centrality measures shown in Fig. 7 .
Node centrality measures
Besides, closeness and betweenness centrality scores, displayed also in Fig. 7 , are of interest to quantitatively characterize the topography of the topic co-occurrence network. Specifically, closeness centrality measures the mean distance from a vertex to other vertices (Zhang and Luo 2017 ), whereas the betweenness centrality of a node measures the extent to which the node is part of paths that connect an arbitrary pair of nodes in the network (Brandes 2001 ); put in other way betweenness measure quantifies the degree to which a node serves as a bridge. It results that the thematic topics such as “social networks and communities” (Topic 4), “religion and extremism” (Topic 7) and “cyberhate” (Topic 10) are ranked first. These findings suggest that those research areas are more effective and accessible in the network and form the densest bridges with other nodes.
We also built the topic co-occurrence networks distinguishing three different stages in the historical development of online hate speech research, as displayed in Fig. 8 . The initial development stage refers to 1992–2009 and accounts for 227 publications; then there was the rapid development stage (2010–2015 years), when the results of research have been rapidly emerging with more than 450 scientific contributes published. Finally, we move into the last three years-period (2016–2019), when more than 300 papers are being published every year. As before, the connections in the network maps represent the interactions between the different research fields and, in each network, the edges whose weight is lower than the average co-occurrence number for the corresponding temporal interval have been suppressed.
Topic co-occurence networks
It can be seen that as new topics emerge, the network structure becomes richer in terms of connections, showing the most important footprints of the related research activities. Through a qualitative analysis of Fig. 8 , we observe that with advances in computer technology, especially developments in data or text mining and information retrieval, research on online hate speech based on computer sciences continues to receive more and more attention. In fact, from the analysis of links in the co-occurrence topic network, it was possible to identify, in the last period, interesting relations especially between Topics 8 and 12.
Overall, in the last thirty years, topics related to online hate research tend to arrange into three main clusters (Fig. 9 ). The fast greedy algorithm implemented in the R package igraph (Csardi and Nepusz 2006 ) was used to group the topics. The first meaningful cluster includes six topics that bring together basic themes of hate speech, covered by Topics 1,2, 3, as well as online speech designed to promote hate on the basis of race (Topic 5) and religion and extremism (Topic 7). At this group belongs also Topic 9, associated with analysis of discourse and language. In the smallest group, we find that cyberhate and gendered online hate are clustered together. Finally, Topics 4, 6, 8 and 12, in the last group, reveals that publications in this cluster deal with machine learning techniques and hateful content on online social media.
Topic clusters
Influential countries in the identified topics.
Table 1 summarises the top-ten countries’ share of publication in the study of online hate speech for each of the identified clusters. Actually, for the themes of the first group (Topics 1, 3, 5, 7 and 9), owing to the presence of ex-fair scores, are displayed the first 11 publisher countries. Not surprisingly, the Anglo-Saxon States are very involved in research dealing with the general debate of “hate speech” versus “freedom of expression”. In fact, in these countries, especially in the United States, the constitutional protection of freedom of speech is vigorously defended. Conversely, other countries, mainly European countries, prohibit certain forms of speech and even the expression of certain opinions, such those to incite hatred, but also to publicly deny crimes of genocide (e.g., the Holocaust) or war crimes.
United States and United Kingdom holds the largest share of publications in the other two domains, suggesting that both these countries had a pioneering role and the strongest impact in the new strands of research focused on machine learning algorithms and text classification as a viable source for identification of hate speech as well as on investigating cyberbullying and gendered hate behaviours. Interestingly, research on automatic identification and classification of hateful languages on social media using machine learning methods emerges as an important component also in the Italian, Indian and Spanish research activity on hate speech. Finally, for the third cluster (Topics 10 and 11), we see that a not negligible number of publications on themes linked with cyberbulling and gendered hate originated from Finland, which occupies the third position in the correspondent ranking, followed by Italy and South Africa.
The preliminary analysis in the previous subsection depicts the overall landscape of countries contribution to the studies on online hate speech. Moving forward, by taking into account authors’ affiliation, it is possible to analyse the level of cooperation between countries. It is worth noting that country research collaboration is a valuable means since it allows scholars to share information and play their academic advantages (Ebadi and Schiffauerova 2015 ), and is deemed the hallmark of contemporary scientific production. To highlight the country research collaboration in the online hate speech research field, we constructed the countries cooperation network, displayed in the Supplementary Material. In what follows, we take into account the cooperation with respect to each of the clusters identified in the “Topic interactions” section. The characteristics of international cooperation between different countries in each domain of online hate research can be argued from the network maps visualised in Figs. 10 , 11 and 12 . We see that the United States is the major partner in international cooperation in the field of online hate speech, in all identified topic clusters. Academic cooperative connections among countries, generating research on Topics 1, 2, 3, 5, 7 and 9, primarily originate from the Unites States, United Kingdom, Germany, Brazil, Sweden and Spain. The top ranked countries by centrality, for the cluster that embraces Topics 4, 6, 8 and 12, are Unites States, United Kingdom, China, Italy, Spain, Germany and Brazil. Finally, for the research related to the remaining Topics 10 and 11, we discover a wider scientific collaboration, mainly, among United States, Spain, South Korea, Czech Republic and Germany.
Country cooperation network for topics 1, 2, 3, 5, 7, 9
Country cooperation network for topics 4,6,8,12
Country cooperation network for topics 10,11
In the last years, the dynamics and usefulness of social media communications are seriously affected by hate speech (Arango et al. 2019 ), which has become a huge concern, attracting worldwide interest. The attention payed to online hate speech by the scientific research community and by policy makers is a reaction to the spread of of hate speech, in all its various forms, on the many social media and other online platforms, and to the pressing need to guarantee non-discriminatory access to digital spaces, as well.
Motivated by these concerns, this paper has presented a bibliometric study of the world’s research activity on online hate speech, performed with the aim of providing an overview of the extent of published research in this field, assessing the research output and suggesting potential, fruitful, future directions.
Beyond the identification and mapping of traditional bibliometric indicators, we focused on the contemporary structure of the field that is composed of a certain variety of themes that researchers are engaging with over the years. Through topic modelling analysis, implemented via LDA algorithm, the main research topics of online hate have been identified and grouped in categories. In contrast to previous researches, designed as qualitative literature review, this study provides a broader and quantitative analysis of publications of online hate speech. In this respect, it should be noted that although topic models do not offer new insights on representing the main area of the research, it gives to our knowledge, for the first time, the possibility of discovering latent and potentially useful contents, shape their possible structure and relationships underlying the data, with quantitative methods.
As pointed out by different authors (see, among others, Yau et al. 2014 ), the combination of topic modelling algorithms and bibliometrics allows the researcher to feature the retrieved topics with a number of topic-based analytic indicators, other than to investigate their significance and dynamic evolution, and model their quantitative relations.
Our analysis has systematically sorted the relevant international studies, producing a visual analysis of 1614 documents published in Scopus database, and generated a large amount of empirical data and information.
The following conclusions can be drawn. The volume of academic papers published in a representative sample, from 1992 to 2019, displays a significant increase after 2010; thus, in the main evolution of online hate speech research, it has been possible to identify an initial development stage (1992–2010) followed by a rapid development (2011–2019). Many countries are regularly involved in publishing in this research field, even if the majority of studies have been conducted in the context of the high-income western countries; in this respect, it is notable the research strength of United States and United Kingdom. Also, the empirical findings provide evidence for the capability of countries to build significant research cooperation. The topic analysis retrieves twelve recurring topics, which can be characterised into three clusters. Specifically, the contemporary structure of online hate literature can be viewed as composed by a group dealing with basic themes of hate speech, a collection of documents that focuses on hate-speech automatic detection and classification by machine-learning strategies and, finally, a third core which focuses on specific themes of gendered hate speech and cyberbullying. Once the groups have been created and identified, the next step is to understand the evolutionary process of each of them over the years. Looking chronologically at online hate research development, we have a trace of an overall shift from generic and knowledge based themes towards approaches that face the challenges of automatic detection of hate speech in text and hate speech addressed to specific targets. The combination of topic modelling algorithms with tools of network analysis enabled to clarify topics relation and has made clear and visible the interdisciplinary nature of the field. The confluence of online hate studies into hate-speech automatic detection and classification approaches stresses how the problem of hate diffusion should be studied not only from the social point of view but also from the point of view of computer science. In our opinion, the main reason driving the shift from conceptually oriented studies to more practically oriented ones is that there is a growing demand for finding statistical methodologies to automatically detect hate speech and make it possible to build effective counter-measures. It is worth noting, however, that the observed shift does not remove the subjective nature of hate speech denotation, given that automatic detection and classification methods need ultimately to rely on a specific definition of what communication should be interpreted as offensive, dangerous and conveying hate. Moreover, supervised techniques require an annotated set of social media contents that will be used to train the algorithms to better detect and score online comments but interpretation of hatefulness varies significantly among individual raters (Salminen et al. 2019 ). There is also evidence highlighting how people from different countries perceive hatefulness of the same online comments differently (Salminen et al. 2018 ). The authors of these studies suggest that online hate should be defined as a subjective experience rather than as an average score that is uniform to all users and that research should concentrate on how incorporate user-level features when scoring and automating the processing of online hate.
An other interesting field worth of investigation is related to the producers of online hate speech. While the online behaviour of organized hate groups has been extensivily analysed, only recently attention has focused on the behaviour of individuals that produce hate speech on the mainstream platforms (see Siegel 2020 , and references herein). Finally, future study should continue to investigate tools devoted to effectively combat online hate speech. Since content deletion or user suspension may be charged with censorship and overblocking, one alternate strategy is to oppose hate content with counter-narratives (Gagliardone et al. 2015 ). Therefore, a promising line of research is the exploration of effective counterspeech techniques which can vary according to hate speech targets, online platforms and haters characteristics.
We think that this work, based on solid data and computational analyses, might provide a clearer vision for researchers involved in this field, providing evidence of the current research frontiers and the challenges that are expected in the future, highlighting all the connections and implications of the research in several research domains.
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Alice Tontodimamma
Department of Economics, University G. d’Annunzio of Chieti–Pescara, Pescara, Italy
Eugenia Nissi
Department of Legal and Social Sciences, University G. d’Annunzio of Chieti–Pescara, Pescara, Italy
Annalina Sarra & Lara Fontanella
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Whether you've been tapped to give a speech at the annual sales convention, or asked to prepare a write a speech for a research paper, you'll need to research your speech to make sure it's as solid as possible. You’ll not only want to be sure that the points you make are backed up by solid evidence, you’ll also want to be sure that your research is presented to listeners in a way that’s easily digestible. We'll help you research your speech topic and draw from a variety of sources so you can write a speech that will leave your audience wanting to hear more.
Join the community, add a new evaluation result row, speech-to-speech translation.
35 papers with code • 3 benchmarks • 5 datasets
Speech-to-speech translation (S2ST) consists on translating speech from one language to speech in another language. This can be done with a cascade of automatic speech recognition (ASR), text-to-text machine translation (MT), and text-to-speech (TTS) synthesis sub-systems, which is text-centric. Recently, works on S2ST without relying on intermediate text representation is emerging.
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Robust speech recognition via large-scale weak supervision.
We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet.
We introduce AudioLM, a framework for high-quality audio generation with long-term consistency.
What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages?
This report introduces FunAudioLLM, a model family designed to enhance natural voice interactions between humans and large language models (LLMs).
We present an attention-based sequence-to-sequence neural network which can directly translate speech from one language into speech in another language, without relying on an intermediate text representation.
Rudrabha/LipGAN • ACM Multimedia, 2019 2019
As today's digital communication becomes increasingly visual, we argue that there is a need for systems that can automatically translate a video of a person speaking in language A into a target language B with realistic lip synchronization.
We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework.
When target text transcripts are available, we design a joint speech and text training framework that enables the model to generate dual modality output (speech and text) simultaneously in the same inference pass.
Using a similarity metric in that multimodal embedding space, we perform mining of audio in German, French, Spanish and English from Librivox against billions of sentences from Common Crawl.
google-research-datasets/cvss • LREC 2022
In addition, CVSS provides normalized translation text which matches the pronunciation in the translation speech.
My Speech Class
Public Speaking Tips & Speech Topics
Jim Peterson has over 20 years experience on speech writing. He wrote over 300 free speech topic ideas and how-to guides for any kind of public speaking and speech writing assignments at My Speech Class.
Some examples of common research paper styles include:
Your research essay topic may also need to be related to the specific class you are taking. For example, an economics class may require a business research paper, while a class on human behavior may call for a psychology research paper.
The requirements for your paper will vary depending on whether you are in high school, college, or a postgraduate student. In high school, you may be able to choose an easy topic and cite five or six sources you found on Google or Yahoo!, but college term papers require more in-depth research from reliable sources, such as scholarly books and peer-reviewed journals.
Do you need some help with brainstorming for topics? Some common research paper topics include abortion, birth control, child abuse, gun control, history, climate change, social media, AI, global warming, health, science, and technology.
But we have many more!
On this page, we have hundreds of good research paper topics across a wide range of subject fields. Each of these topics could be used “as is” to write your paper, or as a starting point to develop your own topic ideas.
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The first step to developing an interesting research paper is choosing a good topic. Finding a topic can be difficult, especially if you don’t know where to start. Finding the Right Research Paper Topic
If you are in a class that allows you to choose your own term paper topic, there are some important areas to consider before you begin your project:
Your Level of Interest: Research papers are time-consuming; you will be spending countless hours researching the topic and related topics, developing several primary and secondary sources, and putting everything together into a paper that is coherent and accomplishes your objectives. If you do not choose a topic you are passionate about, the process will be far more tedious, and the finished product may suffer as a result.
Your Level of Experience: Being interested in a topic is great, but it is even more helpful if you already know something about it. If you can find a topic that you already have some personal and/or professional experience with, it will vastly reduce the amount of research needed and make the whole process much easier.
Available Information on the Topic: Be sure to choose a topic that is not only interesting but also one that has numerous sources available from which to compile your research. A researchable topic with several potential sources gives you access to the level of information you need to become an authority on the subject.
Your Audience: An interesting topic to you may not necessarily be interesting to your professor or whoever is grading your research paper. Before you begin, consider the level of interest of the person(s) who will be reading it. If you are writing a persuasive or argumentative essay, also consider their point of view on the subject matter.
As you begin researching your topic, you may want to revise your thesis statement based on new information you have learned. This is perfectly fine, just have fun and pursue the truth, wherever it leads. If you find that you are not having fun during the research phase, you may want to reconsider the topic you have chosen.
The process of writing the research paper is going to be very time consuming so it’s important to select a topic that is going to sustain your interest for the duration of the project. It is good to select a topic that is relevant to your life since you are going to spend a long time researching and writing about it. Perhaps you are considering starting your own business or pursuing a career in politics. Look through the suggested research paper topics and find one in a category that you can relate to easily. Finding a topic that you have some personal interest in will help make the arduous task a lot easier, and the project will have better results because of your vested interest.
Affirmative action, health, pharmacy, medical treatments, interpersonal communication, marketing and advertising, barack obama, discrimination, bill clinton, hilary clinton, computer crimes and security, cosmetic surgery, controversial, criminal justice, donald trump, easy/simple, environment, family violence, foreign policy, gambling and lotteries, the lgbtq community, generational conflict, gun control, hate crimes, immigration, middle east, maternity/paternity leave, natural disasters, police work, population explosion, pornography, prisons and prisoners, prostitution, ronald reagan, student loan debt, teen issues, women, mothers, what, why, and how, relationships.
We compiled an exhaustive list of topics that would make excellent research papers. The topics are specifically organized to help you find one that will work for your project. Broad topics are headed, and then below them are narrowed topics, all to help you find an area to focus on. The way we have organized the topics for research papers can save you lots of time getting prepared to write your research paper.
We have topics that fit into categories that cover such areas as education, environmental sciences, communication and languages, current events, politics, business, criminal justice, art, psychology, economics to name just a few. Simply get started by choosing the category that interests you and peruse through the topics listed in that category and you’ll be well on your way to constructing an excellent research paper.
Be sure to check other topics ideas: persuasive speech topics , argumentative speech topics , policy speech topics . We also have some sample outlines and essay templates .
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How has music evolved? How has music effected history? Music of the past vs music of the present. How has the music industry effected the music’s quality?
Do you think abortion is legal? Why they do abortion?
Why are people instinctively afraid of animals that are not mammals?
Should abortion be legalized? Should domestic abuse and child abuse victims be granted clemency for killing their abuser?
Jewish holocaust and its contribution to European History, specifically Germany
What is the most popular college in the United States?
The Black Knight: Space Waste or Alien Satellite? The Moon Landing: Real or Hollywood Hoax? Have We Become Too Politically Correct? Paranormal Research: Real? Fake? Should it be offered in college? Who really was Jack the Ripper? Can a zombie apocalypse truly occur? Who is the best or worst president of the USA? The Men in Black: real or hoax?
Why Marching Band is a sport.
Marching band is not a sport
how did aids start?
Topic : Alternative medicine Research question : Does the alternative medicine is safe and standardized Hypothesis : analyse the quality controle of alternative medicine formulations
Does our nostalgic music/childhood songs affect our present lifestyle, and in what ways?
reverse discriminations is still discrimination so there’s no such thing as that. like reverse racism isn’t a thing because that is still racism
Men on birth control and not women.
You forget the topic Islamophobia 😉
You should add a music section. Is Muzio Clementi overshadowed by Mozart? The Toccata and Fugue in D- really wasn’t written by Bach The use of the “Dies Irae” in cinema Why is modern music so repetitive and simple compared to classical music?
I want to do a research project on Education
I want to research but not get a perfect topic help me give me a best topic about current affairs
Topic: History. Are the Crusades oversimplified? where they justified? If so, how? Topic: Current affairs. Is the term “conspiracy theory” used to discredit any non-mainstream, controversial opinions. Topic: Gun control. Does limiting magazine capacity for firearms have any effect on gun crime? Are high-capacity magazines ever necessary for self-defense? Topic: Economics. Are minimum wage laws necessary to guarantee “decent”, or do the laws of supply and demand automatically ensure that?
Are women funny?
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Project 2025 has a starring role in this week’s Democratic National Convention.
And it was front and center on Night 1.
WATCH: Hauling large copy of Project 2025, Michigan state Sen. McMorrow speaks at 2024 DNC
“This is Project 2025,” Michigan state Sen. Mallory McMorrow, D-Royal Oak, said as she laid a hardbound copy of the 900-page document on the lectern. “Over the next four nights, you are going to hear a lot about what is in this 900-page document. Why? Because this is the Republican blueprint for a second Trump term.”
Vice President Kamala Harris, the Democratic presidential nominee, has warned Americans about “Trump’s Project 2025” agenda — even though former President Donald Trump doesn’t claim the conservative presidential transition document.
“Donald Trump wants to take our country backward,” Harris said July 23 in Milwaukee. “He and his extreme Project 2025 agenda will weaken the middle class. Like, we know we got to take this seriously, and can you believe they put that thing in writing?”
Minnesota Gov. Tim Walz, Harris’ running mate, has joined in on the talking point.
“Don’t believe (Trump) when he’s playing dumb about this Project 2025. He knows exactly what it’ll do,” Walz said Aug. 9 in Glendale, Arizona.
Trump’s campaign has worked to build distance from the project, which the Heritage Foundation, a conservative think tank, led with contributions from dozens of conservative groups.
Much of the plan calls for extensive executive-branch overhauls and draws on both long-standing conservative principles, such as tax cuts, and more recent culture war issues. It lays out recommendations for disbanding the Commerce and Education departments, eliminating certain climate protections and consolidating more power to the president.
Project 2025 offers a sweeping vision for a Republican-led executive branch, and some of its policies mirror Trump’s 2024 agenda, But Harris and her presidential campaign have at times gone too far in describing what the project calls for and how closely the plans overlap with Trump’s campaign.
PolitiFact researched Harris’ warnings about how the plan would affect reproductive rights, federal entitlement programs and education, just as we did for President Joe Biden’s Project 2025 rhetoric. Here’s what the project does and doesn’t call for, and how it squares with Trump’s positions.
To distance himself from Project 2025 amid the Democratic attacks, Trump wrote on Truth Social that he “knows nothing” about it and has “no idea” who is in charge of it. (CNN identified at least 140 former advisers from the Trump administration who have been involved.)
The Heritage Foundation sought contributions from more than 100 conservative organizations for its policy vision for the next Republican presidency, which was published in 2023.
Project 2025 is now winding down some of its policy operations, and director Paul Dans, a former Trump administration official, is stepping down, The Washington Post reported July 30. Trump campaign managers Susie Wiles and Chris LaCivita denounced the document.
WATCH: A look at the Project 2025 plan to reshape government and Trump’s links to its authors
However, Project 2025 contributors include a number of high-ranking officials from Trump’s first administration, including former White House adviser Peter Navarro and former Housing and Urban Development Secretary Ben Carson.
A recently released recording of Russell Vought, a Project 2025 author and the former director of Trump’s Office of Management and Budget, showed Vought saying Trump’s “very supportive of what we do.” He said Trump was only distancing himself because Democrats were making a bogeyman out of the document.
The Harris campaign shared a graphic on X that claimed “Trump’s Project 2025 plan for workers” would “go after birth control and ban abortion nationwide.”
The plan doesn’t call to ban abortion nationwide, though its recommendations could curtail some contraceptives and limit abortion access.
What’s known about Trump’s abortion agenda neither lines up with Harris’ description nor Project 2025’s wish list.
Project 2025 says the Department of Health and Human Services Department should “return to being known as the Department of Life by explicitly rejecting the notion that abortion is health care.”
It recommends that the Food and Drug Administration reverse its 2000 approval of mifepristone, the first pill taken in a two-drug regimen for a medication abortion. Medication is the most common form of abortion in the U.S. — accounting for around 63 percent in 2023.
If mifepristone were to remain approved, Project 2025 recommends new rules, such as cutting its use from 10 weeks into pregnancy to seven. It would have to be provided to patients in person — part of the group’s efforts to limit access to the drug by mail. In June, the U.S. Supreme Court rejected a legal challenge to mifepristone’s FDA approval over procedural grounds.
WATCH: Trump’s plans for health care and reproductive rights if he returns to White House The manual also calls for the Justice Department to enforce the 1873 Comstock Act on mifepristone, which bans the mailing of “obscene” materials. Abortion access supporters fear that a strict interpretation of the law could go further to ban mailing the materials used in procedural abortions, such as surgical instruments and equipment.
The plan proposes withholding federal money from states that don’t report to the Centers for Disease Control and Prevention how many abortions take place within their borders. The plan also would prohibit abortion providers, such as Planned Parenthood, from receiving Medicaid funds. It also calls for the Department of Health and Human Services to ensure that the training of medical professionals, including doctors and nurses, omits abortion training.
The document says some forms of emergency contraception — particularly Ella, a pill that can be taken within five days of unprotected sex to prevent pregnancy — should be excluded from no-cost coverage. The Affordable Care Act requires most private health insurers to cover recommended preventive services, which involves a range of birth control methods, including emergency contraception.
Trump has recently said states should decide abortion regulations and that he wouldn’t block access to contraceptives. Trump said during his June 27 debate with Biden that he wouldn’t ban mifepristone after the Supreme Court “approved” it. But the court rejected the lawsuit based on standing, not the case’s merits. He has not weighed in on the Comstock Act or said whether he supports it being used to block abortion medication, or other kinds of abortions.
“When you read (Project 2025),” Harris told a crowd July 23 in Wisconsin, “you will see, Donald Trump intends to cut Social Security and Medicare.”
The Project 2025 document does not call for Social Security cuts. None of its 10 references to Social Security addresses plans for cutting the program.
Harris also misleads about Trump’s Social Security views.
In his earlier campaigns and before he was a politician, Trump said about a half-dozen times that he’s open to major overhauls of Social Security, including cuts and privatization. More recently, in a March 2024 CNBC interview, Trump said of entitlement programs such as Social Security, “There’s a lot you can do in terms of entitlements, in terms of cutting.” However, he quickly walked that statement back, and his CNBC comment stands at odds with essentially everything else Trump has said during the 2024 presidential campaign.
Trump’s campaign website says that not “a single penny” should be cut from Social Security. We rated Harris’ claim that Trump intends to cut Social Security Mostly False.
Project 2025 does propose changes to Medicare, including making Medicare Advantage, the private insurance offering in Medicare, the “default” enrollment option. Unlike Original Medicare, Medicare Advantage plans have provider networks and can also require prior authorization, meaning that the plan can approve or deny certain services. Original Medicare plans don’t have prior authorization requirements.
The manual also calls for repealing health policies enacted under Biden, such as the Inflation Reduction Act. The law enabled Medicare to negotiate with drugmakers for the first time in history, and recently resulted in an agreement with drug companies to lower the prices of 10 expensive prescriptions for Medicare enrollees.
Trump, however, has said repeatedly during the 2024 presidential campaign that he will not cut Medicare.
The Harris campaign said Project 2025 would “eliminate the U.S. Department of Education” — and that’s accurate. Project 2025 says federal education policy “should be limited and, ultimately, the federal Department of Education should be eliminated.” The plan scales back the federal government’s role in education policy and devolves the functions that remain to other agencies.
Aside from eliminating the department, the project also proposes scrapping the Biden administration’s Title IX revision, which prohibits discrimination based on sexual orientation and gender identity. It also would let states opt out of federal education programs and calls for passing a federal parents’ bill of rights similar to ones passed in some Republican-led state legislatures.
Republicans, including Trump, have pledged to close the department, which gained its status in 1979 within Democratic President Jimmy Carter’s presidential Cabinet.
In one of his Agenda 47 policy videos, Trump promised to close the department and “to send all education work and needs back to the states.” Eliminating the department would have to go through Congress.
In the graphic, the Harris campaign says Project 2025 allows “employers to stop paying workers for overtime work.”
The plan doesn’t call for banning overtime wages. It recommends changes to some Occupational Safety and Health Administration, or OSHA, regulations and to overtime rules. Some changes, if enacted, could result in some people losing overtime protections, experts told us.
The document proposes that the Labor Department maintain an overtime threshold “that does not punish businesses in lower-cost regions (e.g., the southeast United States).” This threshold is the amount of money executive, administrative or professional employees need to make for an employer to exempt them from overtime pay under the Fair Labor Standards Act.
In 2019, the Trump’s administration finalized a rule that expanded overtime pay eligibility to most salaried workers earning less than about $35,568, which it said made about 1.3 million more workers eligible for overtime pay. The Trump-era threshold is high enough to cover most line workers in lower-cost regions, Project 2025 said.
The Biden administration raised that threshold to $43,888 beginning July 1, and that will rise to $58,656 on Jan. 1, 2025. That would grant overtime eligibility to about 4 million workers, the Labor Department said.
It’s unclear how many workers Project 2025’s proposal to return to the Trump-era overtime threshold in some parts of the country would affect, but experts said some would presumably lose the right to overtime wages.
Other overtime proposals in Project 2025’s plan include allowing some workers to choose to accumulate paid time off instead of overtime pay, or to work more hours in one week and fewer in the next, rather than receive overtime.
Trump’s past with overtime pay is complicated. In 2016, the Obama administration said it would raise the overtime to salaried workers earning less than $47,476 a year, about double the exemption level set in 2004 of $23,660 a year.
But when a judge blocked the Obama rule, the Trump administration didn’t challenge the court ruling. Instead it set its own overtime threshold, which raised the amount, but by less than Obama.
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Table of contents, listen to research papers aloud: we show you how, types of research papers, how text to speech works, technical language, length and density, time constraints, accessibility issues, proofreading, benefits of listening while reading research papers, text highlighting, speed controls, lifelike voices, ocr scanning, how you can listen to research papers aloud with the speechify website, how you can listen to research papers with the speechify chrome extension, how you can listen to research papers aloud with the speechify app, scan and listen to printed research papers with the speechify app, try speechify and read any text aloud, frequently asked questions.
Listen to research papers aloud and boost productivity and comprehension with our TTS .
In the realm of academia, research papers are a cornerstone for disseminating knowledge and contributing to the growth of various fields. However, the dense and technical nature of these papers can pose a challenge for many readers. Fortunately, text to speech (TTS) technology has emerged as a powerful tool to aid in the consumption of all academic papers. This article will explore different types of research papers, delve into the challenges of reading them, and highlight the benefits of using TTS, with a special focus on Speechify as a premier TTS app for academic purposes.
Research papers are a cornerstone of academic exploration, acting as vehicles for the dissemination of knowledge and the advancement of various fields. Within the realm of scholarly writing, a diverse array of research papers exists, each tailored to specific objectives and methodologies, including:
Text to speech (TTS) is a technology that converts written text into spoken language. This innovative system enables computers, devices, or applications to audibly articulate the content of written material, ranging from articles and documents to emails and web pages.
TTS works by processing the input text through algorithms that analyze linguistic elements, such as syntax and semantics, to generate a corresponding audio output. The synthesized speech can be delivered in a variety of voices and accents, often aiming for a natural and human-like sound.
TTS serves a crucial role in enhancing accessibility, aiding individuals with visual impairments or learning disabilities, and providing a versatile solution for consuming written content in situations where reading may be impractical or inconvenient.
Studying often involves grappling with the challenges presented by research papers. As we navigate through these dense repositories of knowledge crucial for intellectual growth, one powerful ally emerges to mitigate these challenges: text to speech (TTS) technology. Let’s unravel the challenges posed by academic texts and delve into how TTS emerges as a transformative tool, enhancing accessibility, efficiency, and overall engagement:
One of the primary challenges of reading research papers is the abundance of technical language and specialized terminology. For individuals not well-versed in the specific field, deciphering these terms can be a daunting task. Text to speech (TTS) technology addresses this challenge by providing an auditory component to the reading process. Hearing the content aloud can aid in pronunciation, contextual understanding, and overall comprehension of intricate terms. By engaging multiple senses, TTS assists readers in navigating the intricate linguistic landscape of academic papers.
Research papers are often lengthy and densely packed with information, requiring dedicated time and mental focus to absorb the content fully. TTS can alleviate this challenge by allowing users to listen to papers while performing other tasks or listen at a faster rate than physical reading allows. By breaking down the information into manageable auditory segments, TTS enables users to absorb complex concepts without the need for prolonged, uninterrupted reading sessions.
Busy schedules, whether due to academic, professional, or personal commitments, can limit the time available for in-depth reading and analysis of research papers. TTS provides a solution by offering a more time-efficient means of consuming academic content. Users can listen to research papers during activities such as commuting, exercising, or doing household chores, maximizing the utility of their time and seamlessly integrating learning into their daily routines.
Traditional reading methods can pose accessibility challenges for individuals with conditions such as dyslexia, vision issues, or attention disorders. TTS technology serves as an inclusive solution, offering an alternative mode of content consumption. By listening to research papers, individuals with learning differences can overcome barriers related to text-based challenges, making academic content more accessible and fostering a more equitable learning environment. TTS also addresses eye strain issues associated with prolonged reading, promoting a more comfortable reading experience.
Writing research articles can be difficult and re-reading them for typos can seem even more daunting. Text to speech platforms offer a distinct advantage in catching typos and grammatical errors that might be easily missed during traditional visual proofreading. By listening to your research paper, you engage a different cognitive process, allowing you to detect discrepancies in syntax, grammar, and word choice more effectively. This dual approach to proofreading, both visual and auditory, enhances the overall accuracy of your written work, ensuring that typos are promptly identified and rectified, contributing to the production of polished and error-free research papers.
Listening while reading research papers can significantly enhance the learning experience. Combining auditory input with the visual engagement of reading creates a multimodal learning approach that caters to different learning styles. The act of listening to text to speech read research papers aloud can help improve concentration and maintain focus during the often rigorous and dense process of digesting such content. This dual-input method not only reinforces comprehension but also aids in retaining information by tapping into multiple cognitive channels. Additionally, it can make the learning process more dynamic and enjoyable, potentially reducing the perceived difficulty of understanding complex topics.
In the ever-expanding landscape of text to speech (TTS) applications, Speechify emerges as a standout contender, particularly for the discerning academic reader. Navigating the intricate realm of research papers demands a tool that not only provides seamless functionality but also caters to the diverse needs of scholars and learners. Speechify, with its comprehensive set of features and user-friendly design, stands out as the premier TTS app for reading research papers. Here are just a few unique features that position Speechify as the go-to TTS app for the academic community, elevating the reading experience for research papers to unprecedented heights:
Speechify offers text highlighting synchronized with the audio, facilitating better retention and comprehension. This feature is especially beneficial for individuals with dyslexia, ADHD, and other learning differences, who benefit substantially from following along with the text as it is read aloud.
Users can adjust the reading speed to suit their preferences, enabling a customized and comfortable listening experience. Students can easily slow down the reading as they take notes or speed up the reading to meet deadlines or boost productivity.
Speechify boasts a diverse range of 200+ natural-sounding voices indistinguishable from human speech across 30+ various languages and accents, accommodating a global audience and providing an immersive reading experience.
The OCR scanning functionality allows users to convert printed or handwritten text into digital format, enabling students to listen to any digital or physical text aloud.
Speechify, the leading text to speech app, provides an unparalleled solution for listening to research papers aloud, offering a seamless and enriching experience for academic readers. In fact, let’s explore how you can use the Speechify website, Chrome extension , or app to listen to research papers, including how to listen to scanned research papers.
You can listen to research papers straight from the Speechify website. Simply follow the steps below:
If your favorite browser is Google Chrome, you can also listen to research papers by using the Speechify Chrome extension. Here’s a breakdown of how to get started:
If you’d like to read research papers on the go, follow this easy tutorial showing how to use the Speechify app:
You can even read printed research papers with Speechify. Follow this guide to use the Speechify app to scan pictures of your physical documents:
Navigate through dense research papers, craft concise summaries or Google Doc annotations, review social science notes, explore journal articles, read ChatGPT responses, or immerse yourself in academic journals, check emails, and listen to research papers with the help of Speechify. Whether you're a student, researcher, or lifelong learner, Speechify makes it easy to transform any text into speech. Try Speechify for free today and transform your reading experience all while taking advantage of its user-friendly design and innovative features.
Yes, text to speech software, such as NaturalReader or Speechify can read HTML tags and citations aloud, making it easier to follow the structure of the paper and understand the sources cited.
Speechify allows you to easily listen to any physical or digital text aloud. Sign up for free and check it out today.
Text to speech can benefit language learners by improving their pronunciation and listening skills, increasing vocabulary and comprehension, and providing access to a variety of materials in the target language.
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Recurrent neural networks: a comprehensive review of architectures, variants, and applications.
2. related works, 3. fundamentals of rnns, 3.1. basic architecture and working principle of standard rnns, 3.2. activation functions, 3.3. the vanishing and exploding gradient problems, 3.4. bidirectional rnns, 3.5. deep rnns, 4. advanced variants of rnns, 4.1. long short-term memory networks, 4.2. bidirectional lstm, stacked lstm, 4.3. gated recurrent units, comparison with lstm, 4.4. other notable variants, 4.4.1. peephole lstm, 4.4.2. echo state networks.
5. innovations in rnn architectures and training methodologies, 5.1. hybrid architectures, 5.2. neural architecture search, 5.3. advanced optimization techniques, 5.4. rnns with attention mechanisms, 5.5. rnns integrated with transformer models, 6. public datasets for rnn research, 7. applications of rnns in peer-reviewed literature, 7.1. natural language processing, 7.1.1. text generation, 7.1.2. sentiment analysis, 7.1.3. machine translation, 7.2. speech recognition, 7.3. time series forecasting, 7.4. signal processing, 7.5. bioinformatics, 7.6. autonomous vehicles, 7.7. anomaly detection, 8. challenges and future research directions, 8.1. scalability and efficiency, 8.2. interpretability and explainability, 8.3. bias and fairness, 8.4. data dependency and quality, 8.5. overfitting and generalization, 9. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, abbreviations.
AI | Artificial intelligence |
ANN | Artificial neural network |
BiLSTM | Bidirectional long short-term memory |
CNN | Convolutional neural network |
DL | Deep learning |
GRU | Gated recurrent unit |
LSTM | Long short-term memory |
ML | Machine learning |
NAS | Neural architecture search |
NLP | Natural language processing |
RNN | Recurrent neural network |
RL | Reinforcement learning |
SHAPs | Shapley Additive Explanations |
TPU | Tensor processing unit |
VAE | Variational autoencoder |
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Reference | Year | Description |
---|---|---|
Zaremba et al. [ ] | 2014 | Insights into RNNs in language modeling |
Chung et al. [ ] | 2014 | Survey of advancements in RNN training, optimization, and architectures |
Goodfellow et al. [ ] | 2016 | Review on deep learning, including RNNs |
Greff et al. [ ] | 2016 | Extensive comparison of LSTM variants |
Tarwani et al. [ ] | 2017 | In-depth analysis of RNNs in NLP |
Chen et al. [ ] | 2018 | Effectiveness of RNNs in environmental monitoring and climate modeling |
Bai et al. [ ] | 2018 | Comparison of RNNs with other sequence modeling techniques like CNNs and attention mechanisms |
Che et al. [ ] | 2018 | Potential of RNNs in medical applications |
Zhang et al. [ ] | 2020 | RNN applications in robotics, including path planning, motion control, and human–robot interaction |
Dutta et al. [ ] | 2022 | Overview of RNNs, challenges in training, and advancements in LSTM and GRU for sequence learning |
Linardos et al. [ ] | 2022 | RNNs for early warning systems, disaster response, and recovery planning in natural disaster prediction |
Badawy et al. [ ] | 2023 | Integration of RNNs with other ML techniques for predictive analytics and patient monitoring in healthcare |
Ismaeel et al. [ ] | 2023 | Application of RNNs in smart city technologies, including traffic prediction, energy management, and urban planning |
Mers et al. [ ] | 2023 | Performance comparison of various RNN models in pavement performance forecasting |
Quradaa et al. [ ] | 2024 | Start-of-the-art review of RNNs, covering core architectures with a focus on applications in code clones |
Al-Selwi et al. [ ] | 2024 | Review of LSTM applications from 2018 to 2023 |
RNN Type | Key Features | Gradient Stability | Typical Applications |
---|---|---|---|
Basic RNN | Simple structure with short-term memory | High risk of vanishing gradients | Simple sequence tasks like text generation |
LSTM | Long-term memory with input, forget, and output gates | Stable, handles vanishing gradients well | Language translation, speech recognition |
GRU | Simplified LSTM with fewer gates | Stable, handles vanishing gradients effectively | Tasks requiring faster training than LSTM |
Bidirectional RNN | Processes data in both forward and backward directions for better context understanding | Medium stability, depends on depth | Speech recognition and sentiment analysis |
Deep RNN | Multiple RNN layers are stacked to learn hierarchical features | Variable, and the risk of vanishing gradients increases with depth | Complex sequence modeling like video processing |
ESN | Fixed hidden layer weights, trained only at the output | Not applicable as training bypasses typical gradient issues | Time series prediction and system control |
Peephole LSTM | Adds peephole connections to LSTM gates | Stable and similar to LSTM | Recognition of complex temporal patterns like musical notation |
IndRNN | Allows training of deeper networks by maintaining independence between time steps | Reduces risk of vanishing and exploding gradients | Very long sequences, such as in video processing or long text generation |
Dataset Name | Application | Description |
---|---|---|
Penn Treebank [ ] | Natural language processing | A corpus of English sentences annotated for part-of-speech tagging, parsing, and named entity recognition; widely used for language modeling with RNNs |
IMDB Reviews [ ] | Sentiment analysis | A dataset of movie reviews used for binary sentiment classification; suitable for studying the effectiveness of RNNs in text sentiment classification tasks |
MNIST Sequential [ ] | Image recognition | A version of the MNIST dataset formatted as sequences for studying sequence-to-sequence learning with RNNs |
TIMIT Speech Corpus [ ] | Speech recognition | An annotated speech database used for automatic speech recognition systems |
Reuters-21578 Text Categorization Collection [ ] | Text categorization | A collection of newswire articles that is a common benchmark for text categorization and NLP tasks with RNNs |
UCI ML Repository: Time Series Data [ ] | Time series analysis | Contains various time series datasets, including stock prices and weather data, ideal for forecasting with RNNs. |
CORe50 Dataset [ ] | Object Recognition | Used for continuous object recognition, ideal for RNN models dealing with video input sequences where object persistence and temporal context are important |
Application Domain | Reference | Year | Methods and Application |
---|---|---|---|
Text generation | Souri et al. [ ] | 2018 | RNNs for generating coherent and contextually relevant Arabic text |
Holtzman et al. [ ] | 2019 | Controlled text generation using RNNs for style and content control | |
Hu et al. [ ] | 2020 | VAEs combined with RNNs to enhance creativity in text generation | |
Gajendran et al. [ ] | 2020 | Character-level text generation using BiLSTM for various tasks | |
Hussein and Savas [ ] | 2024 | LSTM for text generation | |
Baskaran et al. [ ] | 2024 | LSTM for text generation, achieving excellent performance | |
Islam [ ] | 2019 | Sequence-to-sequence framework using LSTM for improved text generation quality | |
Yin et al. [ ] | 2018 | Attention mechanisms with RNNs for improved text generation quality | |
Guo [ ] | 2015 | Integration of reinforcement learning with RNNs for text generation | |
Keskar et al. [ ] | 2019 | Conditional Transformer Language (CTRL) for generating text in various styles | |
Sentiment analysis | He and McAuley [ ] | 2016 | Adversarial training framework for robustness in sentiment analysis |
Pujari et al. [ ] | 2024 | Hybrid CNN-RNN model for sentiment classification | |
Wankhade et al. [ ] | 2024 | Fusion of CNN and BiLSTM with attention mechanism for sentiment classification | |
Sangeetha and Kumaran [ ] | 2023 | BiLSTM for sentiment analysis by processing text in both directions | |
Yadav et al. [ ] | 2023 | LSTM-based models for sentiment analysis in customer reviews and social media posts | |
Zulqarnain et al. [ ] | 2024 | Attention mechanisms and GRU for enhanced sentiment analysis | |
Samir et al. [ ] | 2021 | Use of pre-trained models like BERT for sentiment analysis | |
Prottasha et al. [ ] | 2022 | Transfer learning with BERT and GPT for sentiment analysis | |
Abimbola et al. [ ] | 2024 | Hybrid LSTM-CNN model for document-level sentiment classification | |
Mujahid et al. [ ] | 2023 | Analyzing sentiment with pre-trained models fine-tuned for specific tasks | |
Machine Translation | Sennrich et al. [ ] | 2015 | Byte-Pair Encoding for handling rare words in translation models |
Wu et al. [ ] | 2016 | Google Neural Machine Translation with deep RNNs for improved accuracy | |
Vaswani et al. [ ] | 2017 | Fully attention-based transformer models for superior translation performance | |
Yang et al. [ ] | 2017 | Hybrid model integrating RNNs into the transformer architecture | |
Song et al. [ ] | 2019 | Incorporating BERT into translation models for enhanced understanding and fluency | |
Kang et al. [ ] | 2023 | Bilingual attention-based machine translation model combining RNN with attention | |
Zulqarnain et al. [ ] | 2024 | Multi-stage feature attention mechanism model using GRU |
Application Domain | Reference | Year | Methods and Application |
---|---|---|---|
Hinton et al. [ ] | 2012 | Deep neural networks, including RNNs, for speech-to-text systems | |
Hannun et al. [ ] | 2014 | DeepSpeech: LSTM-based speech recognition system | |
Amodei et al. [ ] | 2016 | DeepSpeech2: Enhanced LSTM-based speech recognition with bidirectional RNNs | |
Zhang et al. [ ] | 2017 | Convolutional RNN for robust speech recognition | |
Chiu et al. [ ] | 2018 | RNN-transducer models for end-to-end speech recognition | |
Dong et al. [ ] | 2018 | Speech-Transformer: Leveraging self-attention for better processing of audio sequences | |
Bhaskar and Thasleema [ ] | 2023 | LSTM for visual speech recognition using facial expressions | |
Daouad et al. [ ] | 2023 | Various RNN variants for automatic speech recognition | |
Nasr et al. [ ] | 2023 | End-to-end speech recognition using RNNs | |
Kumar et al. [ ] | 2023 | Performance evaluation of RNNs in speech recognition tasks | |
Dhanjal et al. [ ] | 2024 | Comprehensive study of different RNN models for speech recognition | |
Nelson et al. [ ] | 2017 | Hybrid CNN-RNN model for stock price prediction | |
Bao et al. [ ] | 2017 | Combining LSTM with stacked autoencoders for financial time series forecasting | |
Fischer and Krauss [ ] | 2018 | Deep RNNs for predicting stock returns, outperforming traditional ML models | |
Feng et al. [ ] | 2019 | Transfer learning with RNNs for stock prediction | |
Rundo [ ] | 2019 | Combining reinforcement learning with LSTM for trading strategy development | |
Devi et al. [ ] | 2024 | RNN-based model for weather prediction and capturing sequential dependencies in meteorological data | |
Anshuka et al. [ ] | 2022 | LSTM networks for predicting extreme weather events by learning complex temporal patterns | |
Lin et al. [ ] | 2022 | Integrating attention mechanisms with LSTM for enhanced weather forecasting accuracy | |
Marulanda et al. [ ] | 2023 | LSTM model for short-term wind power forecasting and improving prediction accuracy | |
Chen et al. [ ] | 2024 | Bidirectional GRU with TCNs for energy time series forecasting | |
Hasanat et al. [ ] | 2024 | RNNs for forecasting energy demand in smart grids and optimizing renewable energy integration | |
Asiri et al. [ ] | 2024 | Short-term renewable energy predictions using RNN-based models | |
Yildiz et al. [ ] | 2024 | Hybrid model of LSTM with CNN for accurate electricity demand prediction | |
Luo et al. [ ] | 2024 | Attention-based CNN-BiLSTM model for improved financial forecasting | |
Gao et al. [ ] | 2023 | Dynamic ensemble deep ESN for wave height forecasting | |
Bhambu et al. [ ] | 2024 | Recurrent ensemble deep random vector functional link neural network for financial time series forecasting |
Application Domain | Reference | Year | Methods and Application |
---|---|---|---|
Signal processing | Mastoi et al. [ ] | 2019 | ESNs for real-time heart rate variability monitoring |
Valin et al. [ ] | 2021 | ESNs for speech signal enhancement in noisy environments | |
Gao et al. [ ] | 2021 | EWT integrated with ESNs for enhanced time series forecasting | |
Bioinformatics | Li et al. [ ] | 2019 | RNNs for gene prediction and protein-structure prediction |
Zhang et al. [ ] | 2020 | Bidirectional LSTM for predicting DNA-binding protein sequences | |
Xu et al. [ ] | 2021 | RNN-based model for predicting protein secondary structures | |
Yadav et al. [ ] | 2019 | Combining BiLSTM with CNNs for protein sequence analysis | |
Aybey et al. [ ] | 2023 | Ensemble model for predicting protein–protein interactions | |
Autonomous vehicles | Altché and de La Fortelle [ ] | 2017 | LSTM for predicting the future trajectories of vehicles |
Codevilla et al. [ ] | 2018 | RNNs with imitation learning for autonomous driving | |
Li et al. [ ] | 2020 | RNNs for path planning and object detection | |
Lee et al. [ ] | 2020 | Integrating LSTM with CNN for end-to-end autonomous driving | |
Li et al. [ ] | 2024 | Attention-based LSTM for video object tracking | |
Liu and Diao [ ] | 2024 | GRU with deep reinforcement learning for decision-making | |
Anomaly detection | Zhou and Paffenroth [ ] | 2017 | RNNs in unsupervised anomaly detection with deep autoencoders |
Munir et al. [ ] | 2018 | Hybrid CNN-RNN model for anomaly detection in time series | |
Ren et al. [ ] | 2019 | Attention-based RNN model for anomaly detection | |
Li et al. [ ] | 2023 | RNNs with Transfer learning for anomaly detection in manufacturing | |
Mini et al. [ ] | 2023 | RNNs for detecting anomalies in ECG signals | |
Matar et al. [ ] | 2023 | BiLSTM for anomaly detection in multivariate time series | |
Kumaresan et al. [ ] | 2024 | RNNs for detecting network traffic anomalies | |
Altindal et al. [ ] | 2024 | LSTM for anomaly detection in time series data |
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Mienye, I.D.; Swart, T.G.; Obaido, G. Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications. Information 2024 , 15 , 517. https://doi.org/10.3390/info15090517
Mienye ID, Swart TG, Obaido G. Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications. Information . 2024; 15(9):517. https://doi.org/10.3390/info15090517
Mienye, Ibomoiye Domor, Theo G. Swart, and George Obaido. 2024. "Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications" Information 15, no. 9: 517. https://doi.org/10.3390/info15090517
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Introduction Most lawyers grapple with First Amendment Issues Daily according to Professor Marvin Amori as they have to deal with laws that govern and concern speech and consequently write rules on them. Laws on speech are a crucial component in the society due to their significant contribution not only to national matters but also in global corporate boards as well as different public and private institutions. These lawyers therefore have to operate globally and are not only concerned with the First Amendment of the United States constitution but with customs, laws, and practices of different foreign nations (Mark 2238).
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There are several studies that have been conducted to analyse women’s and men’s speech across various cultural identities. The main goal of the studies has been to find out whether there is a difference in the way men and women speak. Speech behaviour of both genders can be analysed in respect to language usage, phonology, verbal choice and the general interactions between men and women in discourse.
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The orator of the speech is Martin Luther King, a man trusted, respected, and considered the most renowned civil rights movement leader of America by the audience. King had developed some promising ethos through the speech (Martin Luther King). For instance, Martin Luther stared the speech through reading from his arranged text, and half way ignored this text during the speech to include a theme “I have a dream”. He was enthusiastic and became more confident as he gained trust and reassuring applause from his audiences (Sundquist).
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Aphasia is a disorder that attacks the sensory system. The person is unable to use the oral, written, auditory comprehension adequately. The interpretation of objects, sounds and feeling is impaired. It is highly associated with diseases such as stroke which leads to paralysis of the brain and nervous system in general. It can also be caused by tumors or cancers that affect the brain as well as any vascular problems affecting the circulation supplying the brain tissues. The problem does not affect the intelligence of the person, but rather their synthesis and interpretation of the same information.
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Title: maskcyclegan-based whisper to normal speech conversion.
Abstract: Whisper to normal speech conversion is an active area of research. Various architectures based on generative adversarial networks have been proposed in the recent past. Especially, recent study shows that MaskCycleGAN, which is a mask guided, and cyclic consistency keeping, generative adversarial network, performs really well for voice conversion from spectrogram representations. In the current work we present a MaskCycleGAN approach for the conversion of whispered speech to normal speech. We find that tuning the mask parameters, and pre-processing the signal with a voice activity detector provides superior performance when compared to the existing approach. The wTIMIT dataset is used for evaluation. Objective metrics such as PESQ and G-Loss are used to evaluate the converted speech, along with subjective evaluation using mean opinion score. The results show that the proposed approach offers considerable benefits.
Comments: | submitted to TENCON 2024 |
Subjects: | Audio and Speech Processing (eess.AS); Machine Learning (cs.LG) |
Cite as: | [eess.AS] |
(or [eess.AS] for this version) | |
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The management of oral cancer is a complex task, often requiring a multidisciplinary effort. To date, advancements in early diagnosis, tailored therapies, and post-treatment care have been achieved through collaboration between surgeons, radiotherapists, oncologists, pathologists, and other specialists. Dentists also play a pivotal role in early diagnosis and post-operative rehabilitation, while speech pathologists are often vital for improving quality of life after treatment; nutrition specialists are fundamental before, during, and after treatment. New frontiers of research in this field have developed new diagnostic and prognostic tools, less invasive ablative and reconstructive surgery, and better tailored systemic treatments. Nonetheless, significant gaps remain in uniform diagnostic standards, optimal treatment modalities tailored to individual patient profiles, and effective rehabilitation measures that optimize long-term quality of life. The scope of this Research Topic is to publish high quality papers, either clinical research, systematic reviews and meta-analyses covering, but will not be limited to, the following topics: 1. Diagnostic methods to detect or to better identify oral potentially malignant disorders at high risk of transformation into cancer, including molecular, genetic, and clinical analysis. 2. Optimal ways to diagnose and follow-up oral cavity cancer through radiology and the potential applications of radiomics. 3. Surgical aspects of oral cavity cancer management including novel ablative and reconstructive techniques. 4. The importance of novel prognostic tools such as lymph node yield and lymph node ratio, tumor infiltrating lymphocytes, and tumor stromal ratio. 5. The role of systemic treatments in improving the survival in specific subsets of oral cancer patients. For example, the emerging role of immunotherapy in the adjuvant or neoadjuvant setting. 6. The importance of dental care, nutritional support, and speech rehabilitation in the perioperative setting for patients affected by oral cancer. 7. The management of treatment complications such as mucositis, xerostomia, or osteoradionecrosis. Any alternative submission proposals are more than welcome; authors are encouraged to submit a manuscript summary proposal via the homepage to check the scope of their potential contribution.
Keywords : oral cancer treatment multidisciplinary approach, comprehensive oral cancer management, oral cancer diagnosis techniques, reconstructive surgery for oral cancer, systemic therapies oral cancer, multidisciplinary oral cancer care team, post-operative rehabilitation, speech therapy, systemic treatments, mucositis, xerostomia, osteoradionecrosis, personalized medicine
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Therefore, in both research and intervention, it is difficult to tease speech and language disorders apart. It is thought that approximately 5% to 8% of children may have difficulties with speech and/or language ( Boyle 1996 ; Tomblin 1997 ), of which a significant proportion will have 'primary' speech and/or language disorders.
A Survey on Neural Speech Synthesis. Xu Tan, Tao Qin, Frank Soong, Tie-Yan Liu. View a PDF of the paper titled A Survey on Neural Speech Synthesis, by Xu Tan and 3 other authors. Text to speech (TTS), or speech synthesis, which aims to synthesize intelligible and natural speech given text, is a hot research topic in speech, language, and ...
View a PDF of the paper titled Robust Speech Recognition via Large-Scale Weak Supervision, by Alec Radford and 5 other authors. We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the ...
During the last decade, Speech Emotion Recognition (SER) has emerged as an integral component within Human-computer Interaction (HCI) and other high-end speech processing systems. Generally, an SER system targets the speaker's existence of varied emotions by extracting and classifying the prominent features from a preprocessed speech signal. However, the way humans and machines recognize and ...
State-of-the-art speech synthesis models try to get as close as possible to the human voice. Hence, modelling emotions is an essential part of Text-To-Speech (TTS) research. In our work, we selected FastSpeech2 as the starting point and proposed a series of modifications for synthesizing emotional speech. According to automatic and human evaluation, our model, EmoSpeech, surpasses existing ...
Create a set of concept word found in your thesis; add synonyms to the list. Example: A properly researched and carefully cited speech will build confidence in the speaker and credibility for the audience. Use quotations around phrases; truncate; use Boolean Logic to broaden or narrow the search
Schematic depiction of the outline of the paper. There are three different phases in this work (a) Pre-training for speaker embeddings using a large non-medical speech data collected from N ...
Abstract. Inner speech, a silent verbal experience, is central to human consciousness and cognition, yet its neural mechanisms remain largely unknown.In this study, we adopted an ecological paradigm called situationally simulated inner speech, which involves the dynamic integration of contextual background, episodic and semantic memories, and external events into a coherent structure.
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech. coqui-ai/TTS • • ICLR 2021 In this paper, we propose FastSpeech 2, which addresses the issues in FastSpeech and better solves the one-to-many mapping problem in TTS by 1) directly training the model with ground-truth target instead of the simplified output from teacher, and 2) introducing more variation information of speech (e ...
A scoping review assesses the potential scope of research done about a certain topic in hopes of retrieving evidence on the team's research topics. To conduct our scoping review, we used the software, Covidence, which allows reviewers to complete article screening and data extraction quickly and flexibly.
In this paper, we provide an overview of the invited and contributed papers presented at the special session at ICASSP-2013, entitled "New Types of Deep Neural Network Learning for Speech ...
The volume of academic papers published in a representative sample, from 1992 to 2019, displays a significant increase after 2010; thus, in the main evolution of online hate speech research, it has been possible to identify an initial development stage (1992-2010) followed by a rapid development (2011-2019).
The research work presented in this paper describes an easy and effective method for speech recognition. The speech is converted to the corresponding text and produces summarized text. This has ...
This paper is intended to give insight to the readers about the development of speech act theories which include categories, characteristics and validities, and strategies.
Conducting Good Research. 1. Keep your research organized. Use a system that works for you. Making sure that you have information organized and sources accounted for will make it much easier for you when it comes time to write your speech. [6] Keep all your research in one place, like a notepad or a word document.
Speech Recognition. 1194 papers with code • 236 benchmarks • 89 datasets. Speech Recognition is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio ...
View a PDF of the paper titled Deep Speech: Scaling up end-to-end speech recognition, by Awni Hannun and 9 other authors. We present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing ...
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Researcher finds sound progress in babies' speech development Date: August 23, 2024 Source: University of Texas at Dallas ... The research was funded by grants from the NIDCD (R01DC015108) and the ...
35 papers with code • 3 benchmarks • 5 datasets. Speech-to-speech translation (S2ST) consists on translating speech from one language to speech in another language. This can be done with a cascade of automatic speech recognition (ASR), text-to-text machine translation (MT), and text-to-speech (TTS) synthesis sub-systems, which is text-centric.
Some common research paper topics include abortion, birth control, child abuse, gun control, history, climate change, social media, AI, global warming, health, science, and technology. But we have many more! On this page, we have hundreds of good research paper topics across a wide range of subject fields. Each of these topics could be used ...
Vice President Kamala Harris, the Democratic presidential nominee, has warned Americans about "Trump's Project 2025" agenda — even though former President Donald Trump doesn't claim the ...
This paper provides a comprehensive overview of ChatGPT, exploring its development, underlying technology, applications, ethical considerations, and future implications.
Interpretative research papers: These involve the interpretation of data, literature, or artistic works, requiring a nuanced understanding of the subject matter. Survey research papers: Based on survey data, these papers analyze and present findings from questionnaires or interviews. How text to speech works
Recurrent neural networks (RNNs) have significantly advanced the field of machine learning (ML) by enabling the effective processing of sequential data. This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures, such as long short-term memory (LSTM) networks, gated recurrent units (GRUs), bidirectional LSTM (BiLSTM), echo state ...
An example of this is the famous Iron Curtain speech made on March 5th 1946, where he changed democratic Western perceptions of the Communist East regions of the Soviet Union. Churchill's speech, entitled "The Sinews of Peace" was a unifying call for the British to be strategic and in their post war actions.
View PDF HTML (experimental) Abstract: Whisper to normal speech conversion is an active area of research. Various architectures based on generative adversarial networks have been proposed in the recent past. Especially, recent study shows that MaskCycleGAN, which is a mask guided, and cyclic consistency keeping, generative adversarial network, performs really well for voice conversion from ...
The scope of this Research Topic is to publish high quality papers, either clinical research, systematic reviews and meta-analyses covering, but will not be limited to, the following topics: 1. Diagnostic methods to detect or to better identify oral potentially malignant disorders at high risk of transformation into cancer, including molecular ...
Abstract and Figures. This survey paper aims to provide a comprehensive overview of the existing research on hate speech detection using machine learning. We review various methodologies and ...
The Kansas City Fed's conference is a golden opportunity for the world's most important central bank to regain control of the policy narrative.