A Cross-Corpus Speech Emotion Recognition Method Based on Supervised Contrastive Learning
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910720749207552 |
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| author | minjie, Xiang |
| author_facet | minjie, Xiang |
| contents | Research on Speech Emotion Recognition (SER) often faces challenges such as the lack of large-scale public datasets and limited generalization capability when dealing with data from different distributions. To solve this problem, this paper proposes a cross-corpus speech emotion recognition method based on supervised contrast learning. The method employs a two-stage fine-tuning process: first, the self-supervised speech representation model is fine-tuned using supervised contrastive learning on multiple speech emotion datasets; then, the classifier is fine-tuned on the target dataset. The experimental results show that the WavLM-based model achieved unweighted accuracy (UA) of 77.41% on the IEMOCAP dataset and 96.49% on the CASIA dataset, outperforming the state-of-the-art results on the two datasets. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_19803 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Cross-Corpus Speech Emotion Recognition Method Based on Supervised Contrastive Learning minjie, Xiang Sound Computation and Language Audio and Speech Processing Research on Speech Emotion Recognition (SER) often faces challenges such as the lack of large-scale public datasets and limited generalization capability when dealing with data from different distributions. To solve this problem, this paper proposes a cross-corpus speech emotion recognition method based on supervised contrast learning. The method employs a two-stage fine-tuning process: first, the self-supervised speech representation model is fine-tuned using supervised contrastive learning on multiple speech emotion datasets; then, the classifier is fine-tuned on the target dataset. The experimental results show that the WavLM-based model achieved unweighted accuracy (UA) of 77.41% on the IEMOCAP dataset and 96.49% on the CASIA dataset, outperforming the state-of-the-art results on the two datasets. |
| title | A Cross-Corpus Speech Emotion Recognition Method Based on Supervised Contrastive Learning |
| topic | Sound Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2411.19803 |