Lessons Learned from the URGENT 2024 Speech Enhancement Challenge

Wangyou Zhang; Kohei Saijo; Samuele Cornell; Robin Scheibler; Chenda Li; Zhaoheng Ni; Anurag Kumar; Marvin Sach; Wei Wang; Yihui Fu; Shinji Watanabe; Tim Fingscheidt; Yanmin Qian

arXiv · 2506.01611

Lessons Learned from the URGENT 2024 Speech Enhancement Challenge

Wangyou Zhang·Kohei Saijo·Samuele Cornell·Robin Scheibler·Chenda Li·Zhaoheng Ni·Anurag Kumar·Marvin Sach·Wei Wang·Yihui Fu·Shinji Watanabe·Tim Fingscheidt·Yanmin Qian

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Abstract

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.

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Wangyou Zhang, Kohei Saijo, Samuele Cornell, Robin Scheibler, Chenda Li, Zhaoheng Ni, Anurag Kumar, Marvin Sach, Wei Wang, Yihui Fu, Shinji Watanabe, Tim Fingscheidt, Yanmin Qian. 2025-06-02. Lessons Learned from the URGENT 2024 Speech Enhancement Challenge. https://arxiv.org/abs/2506.01611

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