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***Speech Emotion Recognition (SER) Datasets:*** *A collection of datasets (count=76) for the purpose of emotion recognition/detection in speech.
***Speech Emotion Recognition (SER) Datasets:*** *A collection of datasets (count=77) for the purpose of emotion recognition/detection in speech.
The table is chronologically ordered and includes a description of the content of each dataset along with the emotions included.
The table can be browsed, sorted and searched under https://superkogito.github.io/SER-datasets/*
| Dataset | Year | Content | Emotions | Format | Size | Language | Paper | Access | License |
|:--------------------------------------------------------------------------------------------------------------------------------------------------|:----------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------|:---------------------|:------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|
| <sub>[nEmo](https://github.com/amu-cai/nEMO)</sub> | <sub>2024</sub> | <sub>3 hours of samples recorded with the participation of nine actors.</sub> | <sub>6 emotions: anger, fear, happiness, sadness, surprised, and neutral.</sub> | <sub>Audio</sub> | <sub>0.434 GB</sub> | <sub>Polish</sub> | <sub>[nEMO: Dataset of Emotional Speech in Polish](https://arxiv.org/abs/2404.06292)</sub> | <sub>Open</sub> | <sub>[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)</sub> |
| <sub>[MDER](https://ieee-dataport.org/documents/moroccan-dialect-emotion-recognition-dataset#files)</sub> | <sub>2024</sub> | <sub>2000 voice records of people speaking Moroccan dialect.</sub> | <sub>5 emotions: Neutral, Happy, Sad, Angry and Fearful.</sub> | <sub>Audio</sub> | <sub>0.187 GB</sub> | <sub>Arabic Moroccan</sub> | <sub>--</sub> | <sub>Open</sub> | <sub>[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)</sub> |
| <sub>[EMOVOME](https://zenodo.org/records/10694370)</sub> | <sub>2024</sub> | <sub>999 spontaneous voice messages from 100 Spanish speakers, collected from real conversations on a messaging app.</sub> | <sub>Valence & arrousal dimensions and 7 emotions: happiness, disgust, anger, surprise, fear, sadness, and neutral.</sub> | <sub>Audio</sub> | <sub>--</sub> | <sub>Spanish</sub> | <sub>[EMOVOME Database: Advancing Emotion Recognition in Speech Beyond Staged Scenarios](https://arxiv.org/abs/2403.02167)</sub> | <sub>Partially open</sub> | <sub>[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)</sub> |
| <sub>[EMNS](http://www.openslr.org/136/)</sub> | <sub>2023</sub> | <sub>1206 high quality labeled utterances by one female speaker (2-3 hours).</sub> | <sub>Anger, excitement, disgust, happiness, surprise, sadness, and neutral (plus sarcasm)</sub> | <sub>Audio</sub> | <sub>0.042 GB</sub> | <sub>English (British)</sub> | <sub>[EMNS /Imz/ Corpus: An emotive single-speaker dataset for narrative storytelling in games, television and graphic novels](https://arxiv.org/abs/2305.13137)</sub> | <sub>Open</sub> | <sub>[Apache 2.0](https://apache.org/licenses/LICENSE-2.0)</sub> |
| <sub>[CAVES](https://rds.westernsydney.edu.au/Institutes/MARCS/2024/Christopher_Davis/)</sub> | <sub>2023</sub> | <sub>Full hd visual recordings of 10 native cantonese speakers uttering 50 sentences.</sub> | <sub>Anger, happiness, sadness, surprise, fear, disgust and neutral</sub> | <sub>Audio</sub> | <sub>47 GB</sub> | <sub>Chinese (cantonese)</sub> | <sub>[A Cantonese Audio-Visual Emotional Speech (CAVES) dataset](https://link.springer.com/article/10.3758/s13428-023-02270-7)</sub> | <sub>Open</sub> | <sub>Available for research purposes only</sub> |
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1 change: 1 addition & 0 deletions src/ser-datasets.csv
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Dataset,Year,Content,Emotions,Format,Size,Language,Paper,Access,License
`nEmo <https://github.com/amu-cai/nEMO>`_,2024,3 hours of samples recorded with the participation of nine actors.,"6 emotions: anger, fear, happiness, sadness, surprised, and neutral.",Audio,0.434 GB,Polish,`nEMO: Dataset of Emotional Speech in Polish <https://arxiv.org/abs/2404.06292>`_,Open,`CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>`_
`MDER <https://ieee-dataport.org/documents/moroccan-dialect-emotion-recognition-dataset#files>`_,2024,2000 voice records of people speaking Moroccan dialect.,"5 emotions: Neutral, Happy, Sad, Angry and Fearful.",Audio,0.187 GB,Arabic Moroccan,--,Open,`CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>`_
`EMOVOME <https://zenodo.org/records/10694370>`_,2024,"999 spontaneous voice messages from 100 Spanish speakers, collected from real conversations on a messaging app.","Valence & arrousal dimensions and 7 emotions: happiness, disgust, anger, surprise, fear, sadness, and neutral.",Audio,--,Spanish,`EMOVOME Database: Advancing Emotion Recognition in Speech Beyond Staged Scenarios <https://arxiv.org/abs/2403.02167>`_,Partially open,`CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>`_
`EMNS <http://www.openslr.org/136/>`_,2023,1206 high quality labeled utterances by one female speaker (2-3 hours).,"Anger, excitement, disgust, happiness, surprise, sadness, and neutral (plus sarcasm)",Audio,0.042 GB,English (British),"`EMNS /Imz/ Corpus: An emotive single-speaker dataset for narrative storytelling in games, television and graphic novels <https://arxiv.org/abs/2305.13137>`_",Open,`Apache 2.0 <https://apache.org/licenses/LICENSE-2.0>`_
`CAVES <https://rds.westernsydney.edu.au/Institutes/MARCS/2024/Christopher_Davis/>`_,2023,Full hd visual recordings of 10 native cantonese speakers uttering 50 sentences.,"Anger, happiness, sadness, surprise, fear, disgust and neutral",Audio,47 GB,Chinese (cantonese),`A Cantonese Audio-Visual Emotional Speech (CAVES) dataset <https://link.springer.com/article/10.3758/s13428-023-02270-7>`_,Open,Available for research purposes only
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