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arXiv · 2510.10740

Dual Data Scaling for Robust Two-Stage User-Defined Keyword Spotting

Zhiqi Ai·Han Cheng·Yuxin Wang·Shiyi Mu·Shugong Xu·Yongjin Zhou
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Abstract

In this paper, we propose DS-KWS, a two-stage framework for robust user-defined keyword spotting. It combines a CTC-based method with a streaming phoneme search module to locate candidate segments, followed by a QbyT-based method with a phoneme matcher module for verification at both the phoneme and utterance levels. To further improve performance, we introduce a dual data scaling strategy: (1) expanding the ASR corpus from 460 to 1,460 hours to strengthen the acoustic model; and (2) leveraging over 155k anchor classes to train the phoneme matcher, significantly enhancing the distinction of confusable words. Experiments on LibriPhrase show that DS-KWS significantly outperforms existing methods, achieving 6.13\% EER and 97.85\% AUC on the Hard subset. On Hey-Snips, it achieves zero-shot performance comparable to full-shot trained models, reaching 99.13\% recall at one false alarm per hour.

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Zhiqi Ai, Han Cheng, Yuxin Wang, Shiyi Mu, Shugong Xu, Yongjin Zhou. 2025-10-12. Dual Data Scaling for Robust Two-Stage User-Defined Keyword Spotting. https://arxiv.org/abs/2510.10740

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