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--- |
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license: cc-by-sa-4.0 |
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language: |
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- en |
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- zh |
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metrics: |
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- f1 |
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library_name: transformers |
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pipeline_tag: audio-classification |
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tags: |
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- speech-emotion-recognition |
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--- |
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# Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on English and Chinese data from elderly speakers. |
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The model is trained on the training sets of [CREMA-D](https://github.com/CheyneyComputerScience/CREMA-D), [CSED](https://github.com/AkishinoShiame/Chinese-Speech-Emotion-Datasets), [ElderReact](https://github.com/Mayer123/ElderReact), and [TESS](https://www.kaggle.com/datasets/ejlok1/toronto-emotional-speech-set-tess). |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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The scripts used for training and evaluation can be found here: |
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[https://github.com/HLTCHKUST/elderly_ser/tree/main](https://github.com/HLTCHKUST/elderly_ser/tree/main) |
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## Evaluation Results |
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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). |
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It also provides the model's speech emotion recognition performances on: English-All, Chinese-All, English-Elderly, Chinese-Elderly, English-Adults, Chinese-Adults. |
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## Citation |
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Our paper will be published at INTERSPEECH 2023. In the meantime, you can find our paper on [arXiv](https://arxiv.org/abs/2306.14517). |
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If you find our work useful, please consider citing our paper as follows: |
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``` |
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@misc{cahyawijaya2023crosslingual, |
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title={Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition}, |
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author={Samuel Cahyawijaya and Holy Lovenia and Willy Chung and Rita Frieske and Zihan Liu and Pascale Fung}, |
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year={2023}, |
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eprint={2306.14517}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
