Lessons Learned from the URGENT 2024 Speech Enhancement Challenge
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918042900889600 |
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| author | Zhang, Wangyou Saijo, Kohei Cornell, Samuele Scheibler, Robin Li, Chenda Ni, Zhaoheng Kumar, Anurag Sach, Marvin Wang, Wei Fu, Yihui Watanabe, Shinji Fingscheidt, Tim Qian, Yanmin |
| author_facet | Zhang, Wangyou Saijo, Kohei Cornell, Samuele Scheibler, Robin Li, Chenda Ni, Zhaoheng Kumar, Anurag Sach, Marvin Wang, Wei Fu, Yihui Watanabe, Shinji Fingscheidt, Tim Qian, Yanmin |
| contents | The URGENT 2024 Challenge aims to foster speech enhancement (SE) techniques with great universality, robustness, and generalizability, featuring a broader task definition, large-scale multi-domain data, and comprehensive evaluation metrics. Nourished by the challenge outcomes, this paper presents an in-depth analysis of two key, yet understudied, issues in SE system development: data cleaning and evaluation metrics. We highlight several overlooked problems in traditional SE pipelines: (1) mismatches between declared and effective audio bandwidths, along with label noise even in various "high-quality" speech corpora; (2) lack of both effective SE systems to conquer the hardest conditions (e.g., speech overlap, strong noise / reverberation) and reliable measure of speech sample difficulty; (3) importance of combining multifaceted metrics for a comprehensive evaluation correlating well with human judgment. We hope that this endeavor can inspire improved SE pipeline designs in the future. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_01611 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lessons Learned from the URGENT 2024 Speech Enhancement Challenge Zhang, Wangyou Saijo, Kohei Cornell, Samuele Scheibler, Robin Li, Chenda Ni, Zhaoheng Kumar, Anurag Sach, Marvin Wang, Wei Fu, Yihui Watanabe, Shinji Fingscheidt, Tim Qian, Yanmin Audio and Speech Processing Sound Signal Processing The URGENT 2024 Challenge aims to foster speech enhancement (SE) techniques with great universality, robustness, and generalizability, featuring a broader task definition, large-scale multi-domain data, and comprehensive evaluation metrics. Nourished by the challenge outcomes, this paper presents an in-depth analysis of two key, yet understudied, issues in SE system development: data cleaning and evaluation metrics. We highlight several overlooked problems in traditional SE pipelines: (1) mismatches between declared and effective audio bandwidths, along with label noise even in various "high-quality" speech corpora; (2) lack of both effective SE systems to conquer the hardest conditions (e.g., speech overlap, strong noise / reverberation) and reliable measure of speech sample difficulty; (3) importance of combining multifaceted metrics for a comprehensive evaluation correlating well with human judgment. We hope that this endeavor can inspire improved SE pipeline designs in the future. |
| title | Lessons Learned from the URGENT 2024 Speech Enhancement Challenge |
| topic | Audio and Speech Processing Sound Signal Processing |
| url | https://arxiv.org/abs/2506.01611 |