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arxiv: 2506.23859 · v2 · pith:OETW6E43new · submitted 2025-06-30 · 📡 eess.AS · cs.SD

Less is More: Data Curation Matters in Scaling Speech Enhancement

Chenda Li , Wangyou Zhang , Wei Wang , Robin Scheibler , Kohei Saijo , Samuele Cornell , Yihui Fu , Marvin Sach
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Zhaoheng Ni Anurag Kumar Tim Fingscheidt Shinji Watanabe Yanmin Qian
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classification 📡 eess.AS cs.SD
keywords dataenhancementspeechmodelsscalingtrainingcurationdatasets
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. FSD50K-Solo: Automated Curation of Single-Source Sound Events

    eess.AS 2026-05 conditional novelty 6.0

    The authors present a scalable curation method that combines diffusion-based mixture synthesis with a discriminative classifier to automatically extract single-source sound events from FSD50K and release the cleaned F...

  2. FSD50K-Solo: Automated Curation of Single-Source Sound Events

    eess.AS 2026-05 unverdicted novelty 6.0

    A curation pipeline combining diffusion-based synthetic mixtures with a discriminative classifier produces and releases FSD50K-Solo, a single-source subset of FSD50K that matches human expert labels on a test set.

  3. Reducing Linguistic Hallucination in LM-Based Speech Enhancement via Noise-Invariant Acoustic-Semantic Distillation

    eess.AS 2026-05 unverdicted novelty 6.0

    L3-SE reduces linguistic hallucination in LM-based speech enhancement by distilling noise-invariant acoustic-semantic representations from noisy inputs to condition an autoregressive decoder-only language model.

  4. Rethinking Training Targets, Architectures and Data Quality for Universal Speech Enhancement

    cs.SD 2026-03 unverdicted novelty 6.0

    Replacing early-reflected speech with time-shifted anechoic clean speech as the training target, combined with a two-stage distortion-perception framework, yields state-of-the-art universal speech enhancement.