ArticlePloS one2018
The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 147 papers, 1 of them a synthesis that pooled it.
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147 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Speech Emotion Recognition in Mental Health: Systematic Review of Voice-Based Applications.JMIR mental health · 2025Pooled it
- Consistency and changes in eye movements during static, dynamic, and audio-visual emotion processing.Perception · 2026Article
- Generative AI emotion recognition from bodily gestures and vocal tone reveals modality-specific performance and positivity bias.Scientific reports · 2026Article
- Explainability guided feature selection for speech emotion recognition using SHAP and permutation importance.Biomedical engineering letters · 2026Article
- A standardized naturalistic audio stimulus dataset with unsupervised labeling.Scientific data · 2026Article
- AlbEmo: A perceptually validated emotional speech dataset for Albanian.Data in brief · 2026Article
- Interpreting the Intensity of Vocal Emotions Across Cultures.Scandinavian journal of psychology · 2026Article
- MindTS-MMD: A Chinese Multimodal Dataset for Emotion Expression Analysis in Children with Tourette Syndrome.Scientific data · 2026Article
- An audiovisual dataset for emotion recognition (UTeMo) in Spanish language.Data in brief · 2026Article
- Emotion-specific modality effects in auditory and visual perception of emotion.Psychological research · 2026Article
- Multimodal emotion recognition using hybrid deep feature fusion under speaker-independent evaluation.Scientific reports · 2026Article
- A 1D-CNN with advanced data augmentation for robust speech emotion recognition.Scientific reports · 2026Article
- Early detection of distinct language development in infants of mothers with severe mental illness using fNIRS.Communications psychology · 2026Article
- Synchrony and Reciprocity in Rhythmic Interaction.Annals of the New York Academy of Sciences · 2026Review
- Theoretical Framework, Technical Evolution, and Future Prospects of Cross-Modal Mapping and Controllable Image Generation Under Multi-Source Heterogeneous Collaboration.Sensors (Basel, Switzerland) · 2026Review
- TraceLAB: A MATLAB Toolbox for Interindividual Synchrony Analysis of Facial Expression and Head Movement Data Acquired via Trace.Entropy (Basel, Switzerland) · 2026Article
- Optimization of speech emotion recognition using hybrid dataset integration and deep learning-based feature fusion with a novel balanced focal entropy loss.Scientific reports · 2026Article
- Dogs' reactions to motivations and emotions in conspecific and heterospecific vocalizations.Scientific reports · 2026Article
- eXCube2: Explainable Brain-Inspired Spiking Neural Network Framework for Emotion Recognition from Audio, Visual and Multimodal Audio-Visual Data.Biomimetics (Basel, Switzerland) · 2026Article
- A Multimodal Adaptive Framework for Social Interaction with the MiRo-E Robot.Sensors (Basel, Switzerland) · 2026Article
87 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
The RAVDESS is a validated multimodal database of emotional speech and song. The database is gender balanced consisting of 24 professional actors, vocalizing 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, with an additional neutral expression. All conditions are available in face-and-voice, face-only, and voice-only formats. The set of 7356 recordings were each rated 10 times on emotional validity, intensity, and genuineness. Ratings were provided by 247 individuals who were characteristic of untrained research participants from North America. A further set of 72 participants provided test-retest data. High levels of emotional validity and test-retest intrarater reliability were reported. Corrected accuracy and composite "goodness" measures are presented to assist researchers in the selection of stimuli. All recordings are made freely available under a Creative Commons license and can be downloaded at https://doi.org/10.5281/zenodo.1188976.
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