SER Evals: In-domain and Out-of-domain Benchmarking for Speech Emotion Recognition
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914913325154304 |
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| author | Osman, Mohamed Kaplan, Daniel Z. Nadeem, Tamer |
| author_facet | Osman, Mohamed Kaplan, Daniel Z. Nadeem, Tamer |
| contents | Speech emotion recognition (SER) has made significant strides with the advent of powerful self-supervised learning (SSL) models. However, the generalization of these models to diverse languages and emotional expressions remains a challenge. We propose a large-scale benchmark to evaluate the robustness and adaptability of state-of-the-art SER models in both in-domain and out-of-domain settings. Our benchmark includes a diverse set of multilingual datasets, focusing on less commonly used corpora to assess generalization to new data. We employ logit adjustment to account for varying class distributions and establish a single dataset cluster for systematic evaluation. Surprisingly, we find that the Whisper model, primarily designed for automatic speech recognition, outperforms dedicated SSL models in cross-lingual SER. Our results highlight the need for more robust and generalizable SER models, and our benchmark serves as a valuable resource to drive future research in this direction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07851 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SER Evals: In-domain and Out-of-domain Benchmarking for Speech Emotion Recognition Osman, Mohamed Kaplan, Daniel Z. Nadeem, Tamer Computation and Language Artificial Intelligence Speech emotion recognition (SER) has made significant strides with the advent of powerful self-supervised learning (SSL) models. However, the generalization of these models to diverse languages and emotional expressions remains a challenge. We propose a large-scale benchmark to evaluate the robustness and adaptability of state-of-the-art SER models in both in-domain and out-of-domain settings. Our benchmark includes a diverse set of multilingual datasets, focusing on less commonly used corpora to assess generalization to new data. We employ logit adjustment to account for varying class distributions and establish a single dataset cluster for systematic evaluation. Surprisingly, we find that the Whisper model, primarily designed for automatic speech recognition, outperforms dedicated SSL models in cross-lingual SER. Our results highlight the need for more robust and generalizable SER models, and our benchmark serves as a valuable resource to drive future research in this direction. |
| title | SER Evals: In-domain and Out-of-domain Benchmarking for Speech Emotion Recognition |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2408.07851 |