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8 results for “emotion database”
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>
Emotional Voice Messages (EMOVOME) database
<p>The Emotional Voice Messages (EMOVOME) database is a speech dataset collected for emotion recognition in real-world conditions. It contains 999 spontaneous voice messages from 100 Spanish speakers, collected from real conversations on a messaging app. EMOVOME includes both expert and non-expert emotional annotations, covering valence and arousal dimensions, along with emotion categories for the expert annotations. Detailed participant information is provided, including sociodemographic data and personality trait assessments using the NEO-FFI questionnaire. Moreover, EMOVOME provides audio recordings of participants reading a given text, as well as transcriptions of all 999 voice messages. Additionally, baseline models for valence and arousal recognition are provided, utilizing both speech and audio transcriptions.</p> <h2>Description</h2> <p>For details on the EMOVOME database, please refer to the article:</p> <blockquote> <p><em>"EMOVOME Database: Advancing Emotion Recognition in Speech Beyond Staged Scenarios". Lucía Gómez-Zaragozá, Rocío del Amor, María José Castro-Bleda, Valery Naranjo, Mariano Alcañiz Raya, Javier Marín-Morales. (pre-print available in <a href="https://doi.org/10.48550/arXiv.2403.02167" target="_blank" rel="noopener">https://doi.org/10.48550/arXiv.2403.02167</a>)<br></em></p> </blockquote> <h2>Content</h2> <p>The Zenodo repository contains four files:</p> <ul> <li><strong>EMOVOME_agreement.pdf</strong>: agreement file required to access the original audio files, detailed in section Usage Notes. </li> <li><strong>labels.csv</strong>: ratings of the three non-experts and the expert annotator, independently and combined.</li> <li><strong>participants_ids.csv</strong>: table mapping each numerical file ID to its corresponding alphanumeric participant ID.</li> <li><strong>transcriptions.csv</strong>:<strong> </strong>transcriptions of each audio.</li> </ul> <p>The repository also includes three folders:</p> <ul> <li><strong>Audios</strong>: it contains the file <strong>features_eGeMAPSv02.csv</strong> corresponding to the standard acoustic feature set used in the baseline model, and two folders: <ul> <li><strong>Lecture</strong>: contains the audio files corresponding to the text readings, with each file named according to the participant's ID.</li> <li><strong>Emotions</strong>: contains the voice recordings from the messaging app provided by the user, named with a file ID.</li> </ul> </li> <li><strong>Questionnaires</strong>: it contains two files: 1) <strong>sociodemographic_spanish.csv</strong> and <strong>sociodemographic_english.csv</strong> are the sociodemographic data of participants in Spanish and English, respectively, including the demographic information; and 2) <strong>NEO-FFI_spanish.csv</strong> includes the participants’ answers to the Spanish version of the NEO-FFI questionnaire. The three files include a column indicating the participant's ID to link the information.</li> <li><strong>Baseline_emotion_recognition</strong>: it includes three files and two folders. The file <strong>partitions.csv</strong> specifies the proposed data partition. Particularly, the dataset is divided into 80% for development and 20% for testing using a speaker-independent approach, i.e., samples from the same speaker are not included in both development and test. The development set includes 80 participants (40 female, 40 male) containing the following distribution of labels: 241 negative, 305 neutral and 261 positive valence; and 148 low, 328 neutral and 331 high arousal. The test set includes 20 participants (10 female, 10 male) with the distribution of labels that follows: 57 negative, 62 neutral and 73 positive valence; and 13 low, 70 neutral and 109 high arousal. Files <strong>baseline_speech.ipynb</strong> and <strong>baseline_text.ipynb</strong> contain the code used to create the baseline emotion recognition models based on speech and text, respectively. The actual trained models for valence and arousal prediction are provided in folders <strong>models_speech</strong> and <strong>models_text</strong>. </li> </ul> <p><em>Audio files in “Lecture” and “Emotions” are only provided to the users that complete the agreement file in section Usage Notes. Audio files are in Ogg Vorbis format at 16-bit and 44.1 kHz or 48 kHz. The total size of the “Audios” folder is about 213 MB. </em></p> <h2>Usage Notes</h2> <p>All the data included in the EMOVOME database is publicly available under the Creative Commons Attribution 4.0 International license. The only exception is the original raw audio files, for which an additional step is required as a security measure to safeguard the speakers' privacy. To request access, interested authors should first complete and sign the agreement file <strong>EMOVOME_agreement.pdf</strong> and send it to the corresponding author (<em><a href="mailto:jamarmo@htech.upv.es" target="_blank" rel="noopener">jamarmo@htech.upv.es</a></em>). The data included in the EMOVOME database is expected to be used for research purposes only. Therefore, the agreement file states that the authors are not allowed to share the data with profit-making companies or organisations. They are also not expected to distribute the data to other research institutions; instead, they are suggested to kindly refer interested colleagues to the corresponding author of this article. By agreeing to the terms of the agreement, the authors also commit to refraining from publishing the audio content on the media (such as television and radio), in scientific journals (or any other publications), as well as on other platforms on the internet. The agreement must bear the signature of the legally authorised representative of the research institution (e.g., head of laboratory/department). Once the signed agreement is received and validated, the corresponding author will deliver the "Audios" folder containing the audio files through a download procedure. A direct connection between the EMOVOME authors and the applicants guarantees that updates regarding additional materials included in the database can be received by all EMOVOME users.</p> <p> </p> <p> </p>
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>
BabyRobot Emotion Database (BRED)
<p>BabyRobot Emotion Database (BRED)</p> <p>This dataset includes extracted data from videos of children performing emotions. The dataset has a total of 215 samples and includes:</p> <ul> <li>Skeletons extracted by <a href="https://github.com/CMU-Perceptual-Computing-Lab/openpose">OpenPose</a>.</li> <li>Facial landmarks extracted by the [OpenFace toolkit](<a href="https://github.com/TadasBaltrusaitis/OpenFace">OpenFace toolkit</a>)</li> <li>Features extracted by a ResNet 50 network pretrained in the <a href="http://mohammadmahoor.com/affectnet/">AffectNet</a> database. Features are in <a href="https://pytorch.org">PyTorch</a> format.</li> <li>Annotations by three different annotators. The annotations are hierarchical and apart from the ground truth emotion, denote if the child used its body and/or face for performing the emotion.</li> <li><strong>Note</strong> that there is an error in the annotations.csv file. Replace "spontaneous" in the path with "game" and "acted" with "pre-game" to get the correct paths.</li> </ul> <p>We also provide the code on <a href="https://github.com/filby89/body-face-emotion-recognition">Github</a>. The accompanied paper can be found on <a href="https://arxiv.org/abs/1901.01805">arXiv</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>
NEMO: A Database for Emotion Analysis Using Functional Near-infrared Spectroscopy
<p>The data from publication "NEMO: A Database for Emotion Analysis Using Functional Near-infrared Spectroscopy".<br> <br> <code>nemo-bids.zip</code> contains the raw optical density (OD) recordings and corresponding metadata for each participant.</p> <p><code><task_id>_csv.zip</code> provides an easy way to access the processed <a href="https://mne.tools/stable/auto_tutorials/epochs/10_epochs_overview.html">epochs</a> data without needing any code from the code repository or other BIDS tools.</p> <p><code>NEMO_additional_metadata.tsv</code> contains additional details, such as subject's age, monitor refresh rate, gender, handedness, recording date and time, specifics about different trial types, and more. Detailed descriptions of each column can be found in the <code>NEMO_additional_metadata_column_descriptions.tsv</code> file.</p> <p>For how to use the data, refer to <a href="https://github.com/Cognitive-Computing-Group/NEMO">https://github.com/Cognitive-Computing-Group/NEMO</a></p>
A Database for Emotion Analysis Using Physiological and Psychological Assessment by 40FY
ClinicalTrials.gov study NCT06172673. IPD Sharing: NO. Countries: 1. Publications: 0.
BioS-DB: a multimodal database of individuals in a public speaking scenario, including emotional annotation
<p>The BioS-DB (BioSpeech Database), is a database of indivduals speaking infront of others in both German and English. BioS-DB includes 55 indivdual (33 male and 22 female), with a mean age of 28.9 years ( ± 10.5 years). Individuals were predominately German Natives (33) - and either students (30) or staff from the computer science department at the University of Augsburg, Germany. The average speech length was 45 s for German and 42 s for English.</p> <p>During the speech, individuals were being evaluated for their emotion (valence / arousal) in a time-continuous way. Individuals were also attached to Blood Volume Pulse, and Skin Conductance sensors, while audio was captured from a lapel microphone and additionally a room microphone.</p> <p>Alice Baird, Shahin Amiriparian, Miriam Berschnider, Maximilian Schmitt, and Björn Schuller (2019),<em> Predicting Biological Signals from Speech: Introducing a Novel Multimodal Dataset and Results</em>, The Multimodal Signal Processing Conference, Kuala Lumpur, Malaysia, Sept 2019. 5 pages.</p> <p><strong>Version 2.0: </strong>This version contains the raw biological signal, and the individual annotations for re-computing the gold-standard. </p> <p>Alice Baird, Shahin Amiriparian, Manuel Milling, Björn Schuller (2020), <em>Emotion Recognition in Public Speaking Scenarios Utilising an LSTM-RNN Approach With Attention, </em>The Speech Language Technology Conference, held virtually (to appear) Jan. 2021. 5 pages. </p>
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