Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913871963357184 |
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| author | Gong, Ziwei Shi, Pengyuan Donbekci, Kaan Ai, Lin Chen, Run Sasu, David Wu, Zehui Hirschberg, Julia |
| author_facet | Gong, Ziwei Shi, Pengyuan Donbekci, Kaan Ai, Lin Chen, Run Sasu, David Wu, Zehui Hirschberg, Julia |
| contents | Speech Emotion Recognition (SER) has seen significant progress with deep learning, yet remains challenging for Low-Resource Languages (LRLs) due to the scarcity of annotated data. In this work, we explore unsupervised learning to improve SER in low-resource settings. Specifically, we investigate contrastive learning (CL) and Bootstrap Your Own Latent (BYOL) as self-supervised approaches to enhance cross-lingual generalization. Our methods achieve notable F1 score improvements of 10.6% in Urdu, 15.2% in German, and 13.9% in Bangla, demonstrating their effectiveness in LRLs. Additionally, we analyze model behavior to provide insights on key factors influencing performance across languages, and also highlighting challenges in low-resource SER. This work provides a foundation for developing more inclusive, explainable, and robust emotion recognition systems for underrepresented languages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02059 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition Gong, Ziwei Shi, Pengyuan Donbekci, Kaan Ai, Lin Chen, Run Sasu, David Wu, Zehui Hirschberg, Julia Sound Computation and Language Speech Emotion Recognition (SER) has seen significant progress with deep learning, yet remains challenging for Low-Resource Languages (LRLs) due to the scarcity of annotated data. In this work, we explore unsupervised learning to improve SER in low-resource settings. Specifically, we investigate contrastive learning (CL) and Bootstrap Your Own Latent (BYOL) as self-supervised approaches to enhance cross-lingual generalization. Our methods achieve notable F1 score improvements of 10.6% in Urdu, 15.2% in German, and 13.9% in Bangla, demonstrating their effectiveness in LRLs. Additionally, we analyze model behavior to provide insights on key factors influencing performance across languages, and also highlighting challenges in low-resource SER. This work provides a foundation for developing more inclusive, explainable, and robust emotion recognition systems for underrepresented languages. |
| title | Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition |
| topic | Sound Computation and Language |
| url | https://arxiv.org/abs/2506.02059 |