
# YueMotion

A Cantonese speech emotion recognition by adult (7 females + 4 males) and elderly (5 females + 2 males) speakers with 5 emotion labels: anger (1), happy (2), sad (3), neutral (4), fear (5), disgust (6).
In total, YueMotion consists of 1080 utterances, i.e., 420 utterances for elderly and 660 for adults.

## Dataset Details

For the details (e.g., the statistics of `train`, `valid`, and `test` data), please refer to our paper on [arXiv](https://arxiv.org/abs/2306.14517).
## Citation

Our paper will be published at INTERSPEECH 2023. In the meantime, you can find our paper on [arXiv](https://arxiv.org/abs/2306.14517).
If you find our work useful, please consider citing our paper as follows:
```
@misc{cahyawijaya2023crosslingual,
      title={Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition}, 
      author={Samuel Cahyawijaya and Holy Lovenia and Willy Chung and Rita Frieske and Zihan Liu and Pascale Fung},
      year={2023},
      eprint={2306.14517},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```
size_categories:1K<n<10K language:yue license:cc-by-sa-4.0 speech speech-emotion-recognition arxiv:2306.14517 region:us
{"language": ["yue"], "license": "cc-by-sa-4.0", "size_categories": ["1K<n<10K"], "pretty_name": "YueMotion", "tags": ["speech", "speech-emotion-recognition"]}
TAGS
#size_categories-1K<n<10K #language-Yue Chinese #license-cc-by-sa-4.0 #speech #speech-emotion-recognition #arxiv-2306.14517 #region-us 


# YueMotion

A Cantonese speech emotion recognition by adult (7 females + 4 males) and elderly (5 females + 2 males) speakers with 5 emotion labels: anger (1), happy (2), sad (3), neutral (4), fear (5), disgust (6).
In total, YueMotion consists of 1080 utterances, i.e., 420 utterances for elderly and 660 for adults.

## Dataset Details

For the details (e.g., the statistics of 'train', 'valid', and 'test' data), please refer to our paper on arXiv.
Our paper will be published at INTERSPEECH 2023. In the meantime, you can find our paper on arXiv.
If you find our work useful, please consider citing our paper as follows:

# YueMotion

A Cantonese speech emotion recognition by adult (7 females + 4 males) and elderly (5 females + 2 males) speakers with 5 emotion labels: anger (1), happy (2), sad (3), neutral (4), fear (5), disgust (6).
In total, YueMotion consists of 1080 utterances, i.e., 420 utterances for elderly and 660 for adults. ## Dataset Details

For the details (e.g., the statistics of 'train', 'valid', and 'test' data), please refer to our paper on arXiv.
Our paper will be published at INTERSPEECH 2023. In the meantime, you can find our paper on arXiv.
If you find our work useful, please consider citing our paper as follows:
TAGS
#size_categories-1K<n<10K #language-Yue Chinese #license-cc-by-sa-4.0 #speech #speech-emotion-recognition #arxiv-2306.14517 #region-us 
 # YueMotion

A Cantonese speech emotion recognition by adult (7 females + 4 males) and elderly (5 females + 2 males) speakers with 5 emotion labels: anger (1), happy (2), sad (3), neutral (4), fear (5), disgust (6).
In total, YueMotion consists of 1080 utterances, i.e., 420 utterances for elderly and 660 for adults. ## Dataset Details

For the details (e.g., the statistics of 'train', 'valid', and 'test' data), please refer to our paper on arXiv.
Our paper will be published at INTERSPEECH 2023. In the meantime, you can find our paper on arXiv.
If you find our work useful, please consider citing our paper as follows:
passage: TAGS
#size_categories-1K<n<10K #language-Yue Chinese #license-cc-by-sa-4.0 #speech #speech-emotion-recognition #arxiv-2306.14517 #region-us 
# YueMotion

A Cantonese speech emotion recognition by adult (7 females + 4 males) and elderly (5 females + 2 males) speakers with 5 emotion labels: anger (1), happy (2), sad (3), neutral (4), fear (5), disgust (6).
In total, YueMotion consists of 1080 utterances, i.e., 420 utterances for elderly and 660 for adults.## Dataset Details

For the details (e.g., the statistics of 'train', 'valid', and 'test' data), please refer to our paper on arXiv.
Our paper will be published at INTERSPEECH 2023. In the meantime, you can find our paper on arXiv.
If you find our work useful, please consider citing our paper as follows: