Less is More: Data Curation Matters in Scaling Speech Enhancement
Abstract
The vast majority of modern speech enhancement systems rely on data-driven neural network models. Conventionally, larger datasets are presumed to yield superior model performance, an observation empirically validated across numerous tasks in other domains. However, recent studies reveal diminishing returns when scaling speech enhancement data. We focus on a critical factor: prevalent quality issues in ``clean'' training labels within large-scale datasets. This work re-examines this phenomenon and demonstrates that, within large-scale training sets, prioritizing high-quality training data is more important than merely expanding the data volume. Experimental findings suggest that models trained on a carefully curated subset of 700 hours can outperform models trained on the 2,500-hour full dataset. This outcome highlights the crucial role of data curation in scaling speech enhancement systems effectively.
Cite
@article{arxiv.2506.23859,
title = {Less is More: Data Curation Matters in Scaling Speech Enhancement},
author = {Chenda Li and Wangyou Zhang and Wei Wang and Robin Scheibler and Kohei Saijo and Samuele Cornell and Yihui Fu and Marvin Sach and Zhaoheng Ni and Anurag Kumar and Tim Fingscheidt and Shinji Watanabe and Yanmin Qian},
journal= {arXiv preprint arXiv:2506.23859},
year = {2025}
}
Comments
Accepted by ASRU2025