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18 results for “emotional speech”
French Emotional Speech Database - Oréau
<p>This document presents the French emotional speech database - Oréau, recorded in a quiet environment. The database is designed for general study of emotional speech and analysis of emotion characteristics for speech synthesis purposes. It contains 79 utterances which could be used in everyday life in the classroom. Between 10 and 13 utterances were written for each of the 7 emotions in French language by 32 non-professional speakers.</p> <p>2 versions are available, the first one contains 502 sentences. A perception test was performed to evaluate the recognition of emotions and their naturalness. 90% of utterances (434 utterances) were correctly identified and retained after the test and various analyses, which constitutes the second version of database.</p>
Shared Acoustic Codes Underlie Emotional Communication in Music and Speech - Evidence from Deep Transfer Learning (Datasets)
<p>This repository contains the datasets used in the article "Shared Acoustic Codes Underlie Emotional Communication in Music and Speech - Evidence from Deep Transfer Learning" (Coutinho & Schuller, 2017). </p> <p>In that article four different data sets were used: SEMAINE, RECOLA, ME14 and MP (acronyms and datasets described below). The SEMAINE (speech) and ME14 (music) corpora were used for the unsupervised training of the Denoising Auto-encoders (domain adaptation stage) - only the audio features extracted from the audio files in these corpora were used and are provided in this repository. The RECOLA (speech) and MP (music) corpora were used for the supervised training phase - both the audio features extracted from the audio files and the Arousal and Valence annotations were used. In this repository, we provide the audio features extracted from the audio files for both corpora, and Arousal and Valence annotations for some of the music datasets (those that the author of this repository is the data curator).</p> <p>Below, you can find description of the various corpora, the details about the data stored in this repository and information on how to obtain the rest of the data used by Coutinho and Schuller (2017).</p> <p><strong>SEMAINE (speech)</strong></p> <p>The SEMAINE corpus (McKeown, Valstar, Cowie, Pantic & Schroder, 2012) was developed specifically to address the task of achieving emotion-rich interactions, and it is adequate for this task as it comprises a wide range of emotional speech. It includes video and speech recordings of spontaneous interactions between human and emotionally stereotyped `characters'. Coutinho & Schuller (2017) used a subset of this database (called <em>Solid-SAL</em>). The <em>Solid-SAL</em> dataset is freely available for scientific research purposes (see http://semaine-db.eu). This repository includes the audio features used in Coutinho & Schuller (2017) (under features/SEMAINE).</p> <p><strong>RECOLA (speech)</strong></p> <p>The RECOLA database (Ringeval, Sonderegger, Sauer & Lalanne, 2013) consists of multimodal recordings (audio, video, and peripheral physiological activity) of spontaneous dyadic interactions between French adults. Coutinho & Schuller (2017) used the RECOLA-Audio module which consists of the audio recordings of each participant in the dyadic phase of the task. In particular, they used the non-segmented high-quality audio signals (WAV format, 44.1kHz, 16bits), obtained through unidirectional headset microphones, of the first five minutes of each interaction. Annotations consist of time-continuous ratings of the level of Arousal and Valence dimensions of emotion perceived by each rater while seeing and listening the audio-visual recordings of each participant task. The publicly available annotated dataset includes only part of the data which amounts to a total number of 23 instances. The time frame length used by Coutinho & Schuller (2017) is 1s (the original annotations were downsampled). This repository includes the audio features used in Coutinho & Schuller (2017) (under features/RECOLA). To obtain the annotations you should contact the author of the original study (see https://diuf.unifr.ch/diva/recola/download.html for further details).</p> <p><strong>ME14 (music)</strong></p> <p>The MediaEval ``Emotion in Music'' task is dedicated to the estimation of Arousal and Valence scores continuously in time and value for song excerpts from the Free Music Archive. Coutinho and Schuller (2017) used the whole corpus (development and test sets for the 2014 challenge) which includes 1,744 songs belonging to 11 musical styles -- Soul, Blues, Electronic, Rock, Classical, Hip-Hop, International, Folk, Jazz, Country, and Pop (maximum of five songs per artist). This repository includes the audio features used in Coutinho & Schuller (2017) (under features/ME14). The full dataset (including annotations) can be obtained from http://www.multimediaeval.org/mediaeval2014/emotion2014/.</p> <p><strong>MP (music)</strong></p> <p>This is a corpus compiled specifically for this work described in Coutinho & Schuller (2017) using data collected in four previous studies. It consists of emotionally diverse full music pieces from a variety of musical styles (Classical and contemporary Western Art, Baroque, Bossa Nova, Rock, Pop, Heavy Metal, and Film Music). Annotations were obtained in controlled laboratory experiments whereby the emotional character of each piece was evaluated time-continuously in terms of levels of Arousal and Valence perceived by listeners (ranging between 35 to 52 in the four studies). In what follows, some details about the various studies are described.</p> <ul> <li>MP<sub>DB1</sub>: This subset of the MP corpus consists of the data reported by Korhonen (2004), and gently made available by the author. This dataset includes six full (or long excerpts) music pieces ranging from 151s to 315s in length (only classical music). Each piece was annotated by 35 participants (14 females). The time series correspondents to each music piece were collected at 1Hz. The golden standard for each piece was computed by averaging the individual time series across all raters. This repository includes the audio features used in Coutinho & Schuller (2017) (under features/MP/DB1). To obtain the labels please contact the author of the original study.</li> <li>MP<sub>DB2</sub>: The dataset by Coutinho & Cangelosi (2011) includes 9 full pieces (43s to 240s long) of classical music (romantic repertoire) annotated by 39 subjects (19 females). Values were recorded every time the mouse was moved with a precision of 1 ms. The resultant timeseries were then resampled (moving average) to a synchronous rate of 1 Hz. The golden standard for each piece was computed by averaging the individual time series across all raters. This repository includes the audio features (under features/MP/DB2) and labels (under annotations/MP/DB2) used in Coutinho & Schuller (2017).</li> <li>MP<sub>DB3</sub>: This dataset was collected by Coutinho & Dibben (2012) and it consists of 8 pieces of film music (84s to 130s long) taken from the late 20th century Hollywood film repertoire. Emotion ratings were given by 52 participants (26 females). The annotation procedure, data processing, and golden standard calculations were identical to MP<sub>DB2</sub>. This repository includes the audio features (under features/MP/DB3) and labels (under annotations/MP/DB3) used in Coutinho & Schuller (2017).</li> <li>MP<sub>DB4</sub>: This dataset was collected by Grewe, Nagel, Kopiez and Altenmüller (2007), and gently made available by the authors. It includes seven music pieces (127s to 502s in length) of heterogeneous styles (e.g., Rock, Pop, Heavy Metal, Classical). Each music piece was annotated by 38 participants (29 females) using an identical methodology to MP<sub>DB2</sub> and MP<sub>DB3</sub>. Data processing and golden standard calculations were also identical. This repository includes the audio features (under features/MP/DB4) used in Coutinho & Schuller (2017). To obtain the labels contact the authors of the original study</li> </ul> <p> </p> <p><strong>Bibliography</strong></p> <p>Coutinho, E., & Cangelosi, A. (2011). Musical emotions: predicting second-by-second subjective feelings of emotion from low-level psychoacoustic features and physiological measurements. <em>Emotion</em>, <em>11</em>(4), 921.</p> <p>Coutinho, E., & Dibben, N. (2013). Psychoacoustic cues to emotion in speech prosody and music. <em>Cognition & Emotion</em>, <em>27</em>(4), 658-684.</p> <p>Coutinho E, Schuller B (2017) Shared acoustic codes underlie emotional communication in music and speech—Evidence from deep transfer learning. PLoS ONE 12(6): e0179289. https://doi. org/10.1371/journal.pone.0179289.</p> <p>Grewe, O., Nagel, F., Kopiez, R., Altenmüller, E. (2007). Emotions over time: synchronicity and development of subjective, physiological, and facial affective reactions to music. <em>Emotion, 7</em>(4), pp. 774-788. DOI: 10.1037/1528-3542.7.4.774.</p> <p>Korhonen, M. (2004). Modeling Continuous Emotional Appraisals of Music Using System Identification. Available from: http://hdl.handle.net/10012/879.</p> <p>McKeown, G., Valstar, M., Cowie, R., Pantic, M., Schroder, M. (2012). The SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent. <em>IEEE Transactions on Affective Computing</em>, 3, pp. 5-17. DOI: http://doi.ieeecomputersociety.org/10.1109/T-AFFC.2011.20.</p> <p>Ringeval, F., Sonderegger, A., Sauer, J. & Lalanne, D. (2013). Introducing the RECOLA Multimodal Corpus of Remote Collaborative and Affective Interactions. In <em>Proceedings of the 2nd International Workshop on Emotion Representation, Analysis and Synthesis in Continuous Time and Space (EmoSPACE 2013)</em>, Shanghai, China. IEEE</p>
Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words
<p>Hi,KIA dataset is a shared short Wakeup Word database focusing on perceived emotion in speech The dataset contains <strong>488 </strong>Wakeup Word speech. </p> <p>For more detailed information about the dataset, please refer to our paper: Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words</p> <p><strong>File Description</strong></p> <ul> <li><em><strong>wav/</strong></em>: wav files. <ul> <li>Filename f`{gender}_{pid}_{scene}_{trial}_{emotion}.wav` The first letter was used to express emotion.<br> </li> </ul> </li> <li><em><strong>annotation/</strong></em>: Information related to annotation and human validation of the entire speech</li> <li> <p><em><strong>split</strong></em>: 8fold data split with {train, valid, test}.csv </p> </li> <li> <p><em><strong>handcraft:</strong></em> Features used for data EDA and baseline performance</p> </li> <li> <p><em><strong>best_weights:</strong></em> wav2vec2.0 context network finetuning weights for re-implementation. Due to file size, we attach only fold M1, F5</p> </li> </ul> <p> </p> <p><strong>Reference</strong></p> <ul> </ul> <p>Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words [[ArXiv](https://arxiv.org/abs/2211.03371)]</p> <p>```<br> @inproceedings{kim2022hi,<br> title={Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words},<br> author={Taesu Kim, SeungHeon Doh, Gyunpyo Lee, Hyung seok Jun, Juhan Nam, Hyeon-Jeong Suk},<br> booktitle={Proceedings of the 14th Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)},<br> year={2022}<br> }<br> ```</p>
Emozionalmente: a crowdsourced Italian speech emotional corpus
<p>This repository contains Emozionalmente: an extensive simulated speech emotional corpus in Italian. The dataset comprises 6,902 labeled samples acted out by 431 amateur actors, each verbalizing 18 different sentences to express the Big Six emotions (anger, disgust, fear, joy, sadness, surprise) plus neutrality. The labels represent the emotional communicative intention of the actors (i.e., the seven emotional states).</p> <p>Key details about the dataset:<br>- **Recording specifications**: The recordings were generally obtained with non-professional equipment. They are .wav files, mono-channel, with a sample size of 16 bits and a sample rate of 16,000 Hz. Each audio recording lasts 3.81 seconds on average (SD = 0.99 seconds).<br>- **Validation**: To validate the emotional content of the clips, 829 humans evaluated each audio recording, providing five evaluations per audio. The general Unweighted Average Recall (UAR) achieved by the evaluators was 66%, which is comparable to previous literature in the field.</p> <p>The repository includes the following additional resources:<br>1. **Demographic information**: Three .csv files describing the demographics of the actors and evaluators, as well as the emotions they expressed and recognized for each audio sample.<br>2. **Data splits**: A speaker-independent train-dev-test split, stratified by emotion, gender, and age.</p> <p>If you use this dataset, please cite the following paper:</p> <blockquote> <p>F. Catania, J. W. Wilke and F. Garzotto,<br><em>"Emozionalmente: A Crowdsourced Corpus of Simulated Emotional Speech in Italian,"</em><br>IEEE Transactions on Audio, Speech and Language Processing, vol. 33, pp. 1142–1155, 2025.<br>doi: <a target="_new" rel="noopener">10.1109/TASLPRO.2025.3540662</a></p> </blockquote>
The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)
<p><strong>Description</strong></p> <p>The Ryerson Audio-Visual Database of Emotional Speech and Song (<a href="https://affectivedatascience.com/datasets.html#ravdess">RAVDESS</a>) contains 7356 files (total size: 24.8 GB). The dataset contains 24 professional actors (12 female, 12 male), vocalizing two lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity (normal, strong), with an additional neutral expression. All conditions are available in three modality formats: Audio-only (16bit, 48kHz .wav), Audio-Video (720p H.264, AAC 48kHz, .mp4), and Video-only (no sound). Note, there are no song files for Actor_18.</p> <p>The RAVDESS was developed by Dr <a href="https://affectivedatascience.com/people/livingstone_sr" target="_blank" rel="noopener">Steven R. Livingstone</a>, who now leads the <a href="https://affectivedatascience.com">Affective Data Science Lab</a>, and Dr <a href="https://www.torontomu.ca/psychology/about-us/our-people/faculty/frank-russo/" target="_blank" rel="noopener">Frank A. Russo</a> who leads the <a href="https://psychlabs.torontomu.ca/smartlab/">SMART Lab</a>.</p> <p><strong>Citing the RAVDESS</strong></p> <p>The RAVDESS is released under a Creative Commons Attribution license, so please cite the RAVDESS if it is used in your work in any form. Published academic papers should use the academic paper citation for our PLoS1 paper. Personal works, such as machine learning projects/blog posts, should provide a URL to this Zenodo page, though a reference to our PLoS1 paper would also be appreciated.</p> <p><em>Academic paper citation</em></p> <blockquote> <p>Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. <a href="https://doi.org/10.1371/journal.pone.0196391">https://doi.org/10.1371/journal.pone.0196391</a>.</p> </blockquote> <p><em>Personal use citation</em></p> <blockquote> <p>Include a link to this Zenodo page - <a href="../record/1188976">https://zenodo.org/record/1188976</a></p> </blockquote> <p><strong>Commercial Licenses</strong></p> <p>Commercial licenses for the RAVDESS can be purchased. For more information, please visit our <a href="https://psychlabs.torontomu.ca/smartlab/ravdess-commercial-licensing/">license page of fees</a>, or contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20Commercial%20License">ravdess@gmail.com</a>.</p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the RAVDESS, to purchase a commercial license, or if you experience any issues downloading files, please contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20feedback%20from%20Zenodo">ravdess@gmail.com</a>.</p> <p><strong>Example Videos</strong></p> <p>Watch a sample of the RAVDESS <a href="https://www.youtube.com/watch?v=Y7OQoNEu3dY">speech</a> and <a href="https://www.youtube.com/watch?v=XQkmH4oYZkg">song</a> videos.</p> <p><strong>Emotion Classification Users</strong></p> <p>If you're interested in using machine learning to classify emotional expressions with the RAVDESS, please see our new RAVDESS Facial Landmark Tracking data set [<a href="../record/3255102">Zenodo project page</a>].</p> <p><strong>Construction and Validation</strong></p> <p>Full details on the construction and perceptual validation of the RAVDESS are described in our PLoS ONE paper - <a href="https://doi.org/10.1371/journal.pone.0196391">https://doi.org/10.1371/journal.pone.0196391</a>.</p> <p>The RAVDESS contains 7356 files. Each file was rated 10 times on emotional validity, intensity, and genuineness. Ratings were provided by 247 individuals who were characteristic of untrained adult research participants from North America. A further set of 72 participants provided test-retest data. High levels of emotional validity, interrater reliability, and test-retest intrarater reliability were reported. Validation data is open-access, and can be downloaded along with our paper from <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0196391">PLoS ONE</a>.</p> <p><strong>Contents</strong></p> <p><em>Audio-only files</em></p> <p>Audio-only files of all actors (01-24) are available as two separate zip files (~200 MB each):</p> <ul> <li>Speech file (Audio_Speech_Actors_01-24.zip, 215 MB) contains 1440 files: 60 trials per actor x 24 actors = 1440. </li> <li>Song file (Audio_Song_Actors_01-24.zip, 198 MB) contains 1012 files: 44 trials per actor x 23 actors = 1012.</li> </ul> <p><em>Audio-Visual and Video-only files</em></p> <p>Video files are provided as separate zip downloads for each actor (01-24, ~500 MB each), and are split into separate speech and song downloads:</p> <ul> <li>Speech files (Video_Speech_Actor_01.zip to Video_Speech_Actor_24.zip) collectively contains 2880 files: 60 trials per actor x 2 modalities (AV, VO) x 24 actors = 2880.</li> <li>Song files (Video_Song_Actor_01.zip to Video_Song_Actor_24.zip) collectively contains 2024 files: 44 trials per actor x 2 modalities (AV, VO) x 23 actors = 2024.</li> </ul> <p><em>File Summary</em></p> <p>In total, the RAVDESS collection includes 7356 files (2880+2024+1440+1012 files).</p> <p><strong>File naming convention</strong></p> <p>Each of the 7356 RAVDESS files has a unique filename. The filename consists of a 7-part numerical identifier (e.g., 02-01-06-01-02-01-12.mp4). These identifiers define the stimulus characteristics: <br><br><em>Filename identifiers </em></p> <ul> <li>Modality (01 = full-AV, 02 = video-only, 03 = audio-only).</li> <li>Vocal channel (01 = speech, 02 = song).</li> <li>Emotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised).</li> <li>Emotional intensity (01 = normal, 02 = strong). NOTE: There is no strong intensity for the 'neutral' emotion.</li> <li>Statement (01 = "Kids are talking by the door", 02 = "Dogs are sitting by the door").</li> <li>Repetition (01 = 1st repetition, 02 = 2nd repetition).</li> <li>Actor (01 to 24. Odd numbered actors are male, even numbered actors are female).</li> </ul> <p><br><em>Filename example: 02-01-06-01-02-01-12.mp4 </em></p> <ol> <li>Video-only (02)</li> <li>Speech (01)</li> <li>Fearful (06)</li> <li>Normal intensity (01)</li> <li>Statement "dogs" (02)</li> <li>1st Repetition (01)</li> <li>12th Actor (12)</li> <li>Female, as the actor ID number is even.</li> </ol> <p><strong>License information</strong></p> <p>The RAVDESS is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> </p> <p>Commercial licenses for the RAVDESS can also be purchased. For more information, please visit our <a href="https://smartlaboratory.org/ravdess/commercial-licensing/">license fee page</a>, or contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20Commercial%20License">ravdess@gmail.com.</a></p> <p><strong>Related Data sets</strong></p> <ul> <li>RAVDESS Facial Landmark Tracking data set [<a href="../record/3255102">Zenodo project page</a>].</li> </ul>
Appendix: Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition
<p>Appendix tables for the paper "Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition".</p> <p>Feature informativeness information was gathered using SHAP values.</p> <p>TABLE VIII: Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral</p> <p>TABLE IX: (continue)Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral</p> <p>TABLE X: Table of Statistics and Individual t-Test Results Between Genders</p> <p>TABLE XI: (continue) Table of Statistics and Individual t-Test Results Between Genders</p> <p>TABLE XII: Table of 5 the most informative feature for each model, according to SHAP values</p> <p>TABLE XIII: (continue)Table of 5 the most informative feature for each model, according to SHAP values</p>
A Kannada Emotional Speech Dataset
<p>There was no emotional speech dataset available in Kannada. This was a limiting factor for research in the Kannada-speaking world. I introduce a Kannada emotional speech dataset and give details about its design and content. This dataset contains six different sentences, pronounced by thirteen people (four male and nine female), in five basic emotions plus one neutral emotion. They are all Kannada speakers. The dataset has been contributed by volunteers and the recordings were not made in a controlled environment. The dataset contains a total of 468 audio samples, each one in a separate audio file. The file naming convention is as follows: AA-EE-SS.wav where AA is a two-character field that gives the actor number (01 to 13), EE is a two-character field that indicates the emotion number (01 to 06), and SS is a two-character field that gives the sentence number (01 to 06). This dataset is freely available under a Creative Commons license.</p> <p>Gender and age of each of the 13 people who contributed to the dataset</p> <ul> <li>01, F, 45</li> <li>02, F, 20</li> <li>03, F, 21</li> <li>04, M, 47</li> <li>05, F, 48</li> <li>06, M, 20</li> <li>07, F, 20</li> <li>08, F, 45</li> <li>09, F, 21</li> <li>10, F, 12</li> <li>11, F, 12</li> <li>12, M, 17</li> <li>13, M, 26</li> </ul> <p>Identification characters for the emotions in the dataset</p> <ul> <li>01, Anger</li> <li>02, Sadness</li> <li>03, Surprise</li> <li>04, Happiness</li> <li>05, Fear</li> <li>06, Neutral</li> </ul> <p>Identification characters for the sentences in the dataset</p> <ul> <li>01, ರೋಗಿಗಳಿಗೆ ಚಿಕಿತ್ಸೆ ನೀಡಿ ಉಪಚರಿಸುವುದು</li> <li>02, ಈ ಕಾದಂಬರಿಯು ಎರಡು ಪಾತ್ರಗಳನ್ನು ಒಳಗೊಂಡಿದೆ</li> <li>03, ಖಾಸಗಿ ವಿಮಾನಗಳೆಂದೂ ಸಾರ್ವಜನಿಕ ವಿಮಾನಗಳೆಂದೂ ವಿಂಗಡಿಸಿದ್ದಾರೆ</li> <li>04, ನಿಮ್ಮನ್ನು ಬೀಟಿಯಾಗಿ ಬಹಳ ಸಂತೋಶ ಆಯಿತು</li> <li>05, ರಾಮನ ಎಡಬಲ ದಲ್ಲಿ ಸೀತಾ ಲಕ್ಷ್ಮಣ ರಿದ್ದಾರೆ</li> <li>06, ಕನ್ನಡವನ್ನು ಕಲಿಯಬೆಕು</li> </ul>
A Canadian French Emotional Speech Dataset
<p>The Canadian French Emotional (CaFE) speech dataset contains six different sentences, pronounced by six male and six female actors, in six basic emotions plus one neutral emotion. The six basic emotions are acted in two different intensities: mild ("Faible") and strong ("Fort").</p> <p>This dataset is freely available under a Creative Commons license (CC BY-NC-SA 4.0).</p> <p>The dataset was digitally recorded at a high-resolution (192 kHz sampling rate, 24 bits per sample).</p> <p>A filtered and downsampled version at a lower resolution (48 kHz sampling rate, 16 bits per sample) is also available.</p> <p>This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.</p> <p>To view a copy of this license, visit <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons</a></p>
emoUERJ: an emotional speech database in Portuguese
<p><strong>GOAL</strong></p> <p>Since language is a key issue in speech emotion recognition (SER) and there are few databases in Portuguese, this database was developed at the State University of Rio de Janeiro aiming to the development of specific SER models for this language.</p> <p> </p> <p><strong>DATABASE DESIGN</strong></p> <p>Ten sentences were made available to eight actors, equally divided between genders, and they were free to choose the phrases for record audios in four emotions target: happiness, anger, sadness or neutral. The following phrases were used:</p> <ul> <li>Não importa quem está certo. (It doesn't matter who is right.)</li> <li>Você perde tempo demais com a Internet. (You waste too much time on the Internet.)</li> <li>A garrafa está na geladeria. (The bottle is in the fridge.)</li> <li>Eu estou me sentindo doente hoje. (I'm feeling sick today)</li> <li>Eu estou um pouco atrasado. (I'm a little late)</li> <li>Nos fins de semana, eu sempre ia para a casa dele(a). (On weekends, I always used to go to his/her house)</li> <li>De quem são essas malas que estão debaixo da mesa? (Whose bags are under the table?)</li> <li>Ele volta na quarta-feira. (He comes back on wednesday)</li> <li>Já chega! Eu vou tomar um banho e ir para a cama. (Enough! I'm going to take a shower and go to bed)</li> <li>Você poderia arrumar a mesa, por favor? (Could you set the table, please?)</li> </ul> <p>The result of this process was 377 audios distributed as follows</p> <ul> <li>happiness: 91</li> <li>anger: 94</li> <li>sadness: 100</li> <li>neutral: 92</li> </ul> <p><strong>FILE IDENTIFICATION</strong></p> <p>Each database file corresponds to a phrase recorded by an actor expressing one of the four emotions and was named as follows:</p> <ul> <li>Position 1: actor's gender ('m' for man or 'w' for woman)</li> <li>Positions 2 and 3: actor's id (from 01 to 04)</li> <li>Position 4: emotion (h: happiness, a: anger, s: sadness, n: neutral)</li> <li>Positions 5 and 6: recording identification</li> </ul> <p>For example, the file 'w04a11' was the eleventh audio recorded by actress 04 interpreting the anger emotion.</p>
Appendix - Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition
<p>Appendix tables for the paper "Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition".</p> <p>Feature informativeness information was gathered using SHAP values.</p> <p>TABLE VIII: Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral</p> <p>TABLE IX: (continue)Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral</p> <p>TABLE X: Table of Statistics and Individual t-Test Results Between Genders</p> <p>TABLE XI: (continue) Table of Statistics and Individual t-Test Results Between Genders</p> <p>TABLE XII: Table of 5 the most informative feature for each model, according to SHAP values</p> <p>TABLE XIII: (continue)Table of 5 the most informative feature for each model, according to SHAP values</p>
ESCorpus-PE: A speech emotional dataset in Spanish with Peruvian accent
<p>ESCorpus-PE dataset contains emotional utterances of Spanish peruvian speech gathered from Spanish interviews, TV reports, political debate and testimonials. It contains 3749 utterances of three emotional dimensions: Valence, Arousal and Dominance. There are 80 speakers (44 male and 36 female). This data was created from Youtube audios. These audios were selected following a specific criteria specified in the paper: ESCorpus-PE: A speech emotional database in Spanish with Peruvian accent, in the section Methods/Audio/Video Selection. Anyone can use this data only for research purposes.</p> <p>More details on <br> https://github.com/Alessandra-UNSA/Peruvian_Spanish_Corpus</p>
Textless Speech-to-Music Retrieval Using Emotion Similarity (Pretrained Model)
<p>- github: https://github.com/SeungHeonDoh/speech-to-music <br> - Demo : https://seungheondoh.github.io/speech-to-music-demo/<br> - ArXiv : update soon</p>
Urdu-Sindhi Speech Emotion Corpus
<p>The <strong>Urdu-Sindhi Speech Emotion Corpus</strong> is a dataset collected at Mehran University of Engineering & Technology, Pakistan by a research team led by Dr. Zafi Sherhan Syed and Dr. Sajjad Ali Memon. The dataset consists on 1,435 audio recordings in total for seven types of emotions which include anger, disgust, happiness, neutral, sarcasm, sadness, and surprise in two low-resource languages of South Asia, that is Urdu and Sindhi.</p> <p>Due to ethical restrictions we cannot release audio recordings at the moment and instead release five feature sets from the OpenSmile toolkit.</p> <p>Please note that we will endeavour to compute any features for you locally and send those features back to you. If this interests you, please contact Dr. Zafi Syed at zafisherhan.shah@faculty.muet.edu.pk</p> <p> </p>
Morgan Emotional Speech Set
<p>This database of emotional speech has been validated for use in experiments of auditory perception of emotion. Category ratings and emotional dimension ratings of activation and pleasantness are available from the researcher upon request.</p> <p>When used academically, please cite using the following citation:</p> <p>Morgan, S. D. (2019). Categorical and Dimensional Ratings of Emotional Speech: Behavioral Findings From the Morgan Emotional Speech Set. Journal of Speech, Language, and Hearing Research, 62(11), 4015-4029.</p> <p> </p>
SPEECH as Biomarker for Emotion, Movement and cOgnition in Parkinson's Disease
ClinicalTrials.gov study NCT05765110. IPD Sharing: NO. Countries: 2. Publications: 0.
Effects of Emotional Processes on Speech Motor Control in Early Childhood Stuttering.
ClinicalTrials.gov study NCT05003583. IPD Sharing: NO. Countries: 1. Publications: 0.
EmoFilm - A multilingual emotional speech corpus
<p><strong>EmoFilm</strong> is a multilingual emotional speech corpus comprising 1115 audio instances produced in English, Italian, and Spanish languages. The audio clips (with a mean length of 3.5 sec. and std 1.2 sec.) were extracted in wave format (uncompressed, mono, 48 kHz sample rate and 16-bit) from 43 films (original in English and their over-dubbed Italian and Spanish versions). Genres including comedy, drama, horror, and thriller were considered; anger, contempt, happiness, fear, and sadness emotional states were taken into account. EmoFilm has been presented at Interspeech 2018:</p> <p>Emilia Parada-Cabaleiro, Giovanni Costantini, Anton Batliner, Alice Baird, and Björn Schuller (2018), <em>Categorical vs Dimensional Perception of Italian Emotional Speech</em>, in Proc. of Interspeech, Hyderabad, India, pp. 3638-3642 .</p> <p>We would like to thank Linda Ratz for her contribution in the generation of the transcriptions.</p> <p> </p> <p><strong>How to access EmoFilm</strong></p> <p>To get access to the dataset, please send the signed End User License Agreement (EULA) when making the request. The EULA <strong>must be signed by somebody from a university holding a permanent position</strong>, typically a full professor. Note that requests without an EULA appropriately filled out, as well as those performed from a non-institutional e-mail address, will be automatically rejected. Please download the EULA from the following link:</p> <p>https://drive.google.com/file/d/1pFHfsqk7snF_EVqq0WAC0Dz8FcTD3s9_/view?usp=share_link</p>
DEMoS: an Italian emotional speech corpus. Elicitation methods, machine learning, and perception
<p>DEMoS (Database of Elicited Mood in Speech), is a corpus of induced emotional speech in Italian. DEMoS encompasses 9,365 emotional and 332 neutral samples produced by 68 native speakers (23 females, 45 males) in seven emotional states: the 'big six' anger, sadness, happiness, fear, surprise, disgust, and the secondary emotion guilt. To get more realistic productions, instead of acted speech, DEMoS contains emotional speech elicited by combinations of Mood Induction Procedures (MIP). Three elicitation methods are presented, made up by the combination of at least three MIPs, and considering six different MIPs in total. To select samples 'typical' of each emotion, evaluation strategies based on self- and external assessment were applied. The selected part of the corpus encompasses 1,564 prototypical samples produced by 59 speakers (21 females, 38 male). DEMoS has been published in the Journal Language, Resousrces, and Evalaution.</p> <p> </p> <p>Emilia Parada-Cabaleiro, Giovanni Costantini, Anton Batliner, Maximilian Schmitt, and Björn Schuller (2019), <em>DEMoS: An Italian emotional speech corpus. Elicitation methods, machine learning, and perception</em>, Language, Resources, and Evaluation, Feb 2019. <a href="http://em.rdcu.be/wf/click?upn=lMZy1lernSJ7apc5DgYM8eCoqdGxOfRWEudjYRrxU-2BI-3D_Ru5N6PJ4ngeR7K-2Fncs2CW1jGAzl4dMvrVh77-2BVH-2B9g5urNss1KItQNXvWL1jiHKvcYDtUVs2c78DX20PMDTauCGehGiQvHdgrAknGggtu7pHINBqVKjp16-2BTn63kNrm22m52e-2FPV-2FidpRe8A-2FplLxPMV-2FjTR-2FLLIK8Wqe7u0-2BLSZ9w-2BWYtrAXRYn2lvPcjGTP1La8yiTxBuJKbHJpnNeFb6LmBIiNMmGRSZPIY0leXhyj4k07rx5cETF6n34aIQHP-2FwcafanNMN4BoA9QKhXGgFxvRgZQidsQ-2BCDbbTBL0PPjM3CgitSGk66qut9E3pd">https://rdcu.be/bn7oI</a></p> <p> </p> <p><strong>How to access DEMoS</strong></p> <p>To get access to the dataset, please send the signed End User License Agreement (EULA) when making the request. The EULA <strong>must be signed by somebody from a university holding a permanent position</strong>, typically a full professor. Note that requests without an EULA appropriately filled out, as well as those performed from a non-institutional e-mail address, will be automatically rejected. Please download the EULA from the following link:</p> <p>https://drive.google.com/file/d/1v6GaCVyNcib5v802t2uXHYOioqIkoBQ-/view?usp=share_link</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.