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***Speech Emotion Recognition (SER) Datasets:*** *A collection of datasets (count=75) for the purpose of emotion recognition/detection in speech.
***Speech Emotion Recognition (SER) Datasets:*** *A collection of datasets (count=76) 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 |
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| <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> |
| <sub>[BANSpEmo](https://data.mendeley.com/datasets/rdwn4bs5ky/2)</sub> | <sub>2023</sub> | <sub>792 utterance recordings from 22 unprofessional speakers (11 males and 11 females) of six basic emotional reactions of two sets of sentences.</sub> | <sub>angry, disgusted, happy, surprised, sad, fear</sub> | <sub>Audio</sub> | <sub>0.555 GB</sub> | <sub>Bangla</sub> | <sub>[BANSpEmo: A Bangla Emotional Speech Recognition Dataset](https://arxiv.org/abs/2312.14020)</sub> | <sub>Open</sub> | <sub>[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)</sub> |
| <sub>[KBES](https://data.mendeley.com/datasets/vsn37ps3rx/4)</sub> | <sub>2023</sub> | <sub>900 audio signals from 35 actors (20 females and 15 males). Each emotion is represented with two intensity levels (low & high)</sub> | <sub>angry, disgusted, happy, neutral, sad</sub> | <sub>Audio</sub> | <sub>0.337 GB</sub> | <sub>Bangla</sub> | <sub>[KBES: A dataset for realistic Bangla speech emotion recognition with intensity level](https://www.sciencedirect.com/science/article/pii/S2352340923008107)</sub> | <sub>Open</sub> | <sub>[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)</sub> |
| <sub>[RESD](https://huggingface.co/datasets/Aniemore/resd_annotated)</sub> | <sub>2022</sub> | <sub>Russian emotional speech dialogue dataset ~3.5 hours of actor-voiced dialogues, each ~3 minutes long, with speech files (16000 or 44100Hz), with speech-to-text transcripts</sub> | <sub>anger, disgust, fear, enthusiasm, happiness, neutral, sadness</sub> | <sub>Audio</sub> | <sub>0.48 GB</sub> | <sub>Russian</sub> | <sub>[EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark](https://arxiv.org/abs/2406.07162)</sub> | <sub>Open</sub> | <sub>[MIT](https://choosealicense.com/licenses/mit/)</sub> |
| <sub>[Hi, KIA](https://zenodo.org/records/7091465)</sub> | <sub>2022</sub> | <sub>A shared short Wakeup Word database focusing on perceived emotion in speech The dataset contains 488 Wakeup Word speech</sub> | <sub>angry, happy, sad, neutral</sub> | <sub>Audio</sub> | <sub>0.75 GB</sub> | <sub>Korean</sub> | <sub>[Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words](https://arxiv.org/abs/2211.03371)</sub> | <sub>Open</sub> | <sub>[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)</sub> |
| <sub>[Emozionalmente](https://zenodo.org/records/6569824)</sub> | <sub>2022</sub> | <sub>6902 labeled samples acted out by 431 amateur actors while verbalizing 18 different sentences</sub> | <sub>anger, disgust, fear, joy, sadness, surprise, neutral</sub> | <sub>Audio</sub> | <sub>0.581 GB</sub> | <sub>Italian</sub> | <sub>--</sub> | <sub>Open</sub> | <sub>[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)</sub> |
| <sub>[BanglaSER](https://data.mendeley.com/datasets/t9h6p943xy/5)</sub> | <sub>2022</sub> | <sub>1467 Bangla speech-audio recordings by 34 non-professional participating actors (17 male and 17 female) from diverse age groups between 19 and 47 years.</sub> | <sub>angry, happy, neutral, sad, surprise</sub> | <sub>Audio</sub> | <sub>0.425 GB</sub> | <sub>Bangla</sub> | <sub>[BanglaSER: A speech emotion recognition dataset for the Bangla language](https://www.sciencedirect.com/science/article/pii/S235234092200302X)</sub> | <sub>Open</sub> | <sub>[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)</sub> |
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Expand Up @@ -5,6 +5,7 @@ Dataset,Year,Content,Emotions,Format,Size,Language,Paper,Access,License
`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
`BANSpEmo <https://data.mendeley.com/datasets/rdwn4bs5ky/2>`_,2023,792 utterance recordings from 22 unprofessional speakers (11 males and 11 females) of six basic emotional reactions of two sets of sentences.,"angry, disgusted, happy, surprised, sad, fear",Audio,0.555 GB,Bangla,`BANSpEmo: A Bangla Emotional Speech Recognition Dataset <https://arxiv.org/abs/2312.14020>`_,Open,`CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>`_
`KBES <https://data.mendeley.com/datasets/vsn37ps3rx/4>`_,2023,900 audio signals from 35 actors (20 females and 15 males). Each emotion is represented with two intensity levels (low & high),"angry, disgusted, happy, neutral, sad",Audio,0.337 GB,Bangla,`KBES: A dataset for realistic Bangla speech emotion recognition with intensity level <https://www.sciencedirect.com/science/article/pii/S2352340923008107>`_,Open,`CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>`_
`RESD <https://huggingface.co/datasets/Aniemore/resd_annotated>`_,2022,"Russian emotional speech dialogue dataset ~3.5 hours of actor-voiced dialogues, each ~3 minutes long, with speech files (16000 or 44100Hz), with speech-to-text transcripts","anger, disgust, fear, enthusiasm, happiness, neutral, sadness",Audio,0.48 GB,Russian,`EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark <https://arxiv.org/abs/2406.07162>`_,Open,`MIT <https://choosealicense.com/licenses/mit/>`_
"`Hi, KIA <https://zenodo.org/records/7091465>`_",2022,A shared short Wakeup Word database focusing on perceived emotion in speech The dataset contains 488 Wakeup Word speech,"angry, happy, sad, neutral",Audio,0.75 GB,Korean,"`Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words <https://arxiv.org/abs/2211.03371>`_",Open,`CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0/>`_
`Emozionalmente <https://zenodo.org/records/6569824>`_,2022,6902 labeled samples acted out by 431 amateur actors while verbalizing 18 different sentences,"anger, disgust, fear, joy, sadness, surprise, neutral",Audio,0.581 GB,Italian,--,Open,`CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>`_
`BanglaSER <https://data.mendeley.com/datasets/t9h6p943xy/5>`_,2022,1467 Bangla speech-audio recordings by 34 non-professional participating actors (17 male and 17 female) from diverse age groups between 19 and 47 years.,"angry, happy, neutral, sad, surprise",Audio,0.425 GB,Bangla,`BanglaSER: A speech emotion recognition dataset for the Bangla language <https://www.sciencedirect.com/science/article/pii/S235234092200302X>`_,Open,`CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>`_
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