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| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.09385 |
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| _version_ | 1866916265602318336 |
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| author | Yang, Shu-wen Chang, Heng-Jui Huang, Zili Liu, Andy T. Lai, Cheng-I Wu, Haibin Shi, Jiatong Chang, Xuankai Tsai, Hsiang-Sheng Huang, Wen-Chin Feng, Tzu-hsun Chi, Po-Han Lin, Yist Y. Chuang, Yung-Sung Huang, Tzu-Hsien Tseng, Wei-Cheng Lakhotia, Kushal Li, Shang-Wen Mohamed, Abdelrahman Watanabe, Shinji Lee, Hung-yi |
| author_facet | Yang, Shu-wen Chang, Heng-Jui Huang, Zili Liu, Andy T. Lai, Cheng-I Wu, Haibin Shi, Jiatong Chang, Xuankai Tsai, Hsiang-Sheng Huang, Wen-Chin Feng, Tzu-hsun Chi, Po-Han Lin, Yist Y. Chuang, Yung-Sung Huang, Tzu-Hsien Tseng, Wei-Cheng Lakhotia, Kushal Li, Shang-Wen Mohamed, Abdelrahman Watanabe, Shinji Lee, Hung-yi |
| contents | The foundation model paradigm leverages a shared foundation model to achieve state-of-the-art (SOTA) performance for various tasks, requiring minimal downstream-specific modeling and data annotation. This approach has proven crucial in the field of Natural Language Processing (NLP). However, the speech processing community lacks a similar setup to explore the paradigm systematically. In this work, we establish the Speech processing Universal PERformance Benchmark (SUPERB) to study the effectiveness of the paradigm for speech. We propose a unified multi-tasking framework to address speech processing tasks in SUPERB using a frozen foundation model followed by task-specialized, lightweight prediction heads. Combining our results with community submissions, we verify that the foundation model paradigm is promising for speech, and our multi-tasking framework is simple yet effective, as the best-performing foundation model shows competitive generalizability across most SUPERB tasks. For reproducibility and extensibility, we have developed a long-term maintained platform that enables deterministic benchmarking, allows for result sharing via an online leaderboard, and promotes collaboration through a community-driven benchmark database to support new development cycles. Finally, we conduct a series of analyses to offer an in-depth understanding of SUPERB and speech foundation models, including information flows across tasks inside the models, the correctness of the weighted-sum benchmarking protocol and the statistical significance and robustness of the benchmark. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09385 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Large-Scale Evaluation of Speech Foundation Models Yang, Shu-wen Chang, Heng-Jui Huang, Zili Liu, Andy T. Lai, Cheng-I Wu, Haibin Shi, Jiatong Chang, Xuankai Tsai, Hsiang-Sheng Huang, Wen-Chin Feng, Tzu-hsun Chi, Po-Han Lin, Yist Y. Chuang, Yung-Sung Huang, Tzu-Hsien Tseng, Wei-Cheng Lakhotia, Kushal Li, Shang-Wen Mohamed, Abdelrahman Watanabe, Shinji Lee, Hung-yi Audio and Speech Processing Computation and Language Signal Processing The foundation model paradigm leverages a shared foundation model to achieve state-of-the-art (SOTA) performance for various tasks, requiring minimal downstream-specific modeling and data annotation. This approach has proven crucial in the field of Natural Language Processing (NLP). However, the speech processing community lacks a similar setup to explore the paradigm systematically. In this work, we establish the Speech processing Universal PERformance Benchmark (SUPERB) to study the effectiveness of the paradigm for speech. We propose a unified multi-tasking framework to address speech processing tasks in SUPERB using a frozen foundation model followed by task-specialized, lightweight prediction heads. Combining our results with community submissions, we verify that the foundation model paradigm is promising for speech, and our multi-tasking framework is simple yet effective, as the best-performing foundation model shows competitive generalizability across most SUPERB tasks. For reproducibility and extensibility, we have developed a long-term maintained platform that enables deterministic benchmarking, allows for result sharing via an online leaderboard, and promotes collaboration through a community-driven benchmark database to support new development cycles. Finally, we conduct a series of analyses to offer an in-depth understanding of SUPERB and speech foundation models, including information flows across tasks inside the models, the correctness of the weighted-sum benchmarking protocol and the statistical significance and robustness of the benchmark. |
| title | A Large-Scale Evaluation of Speech Foundation Models |
| topic | Audio and Speech Processing Computation and Language Signal Processing |
| url | https://arxiv.org/abs/2404.09385 |