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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.10740 |
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| _version_ | 1866908589055016960 |
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| author | Ai, Zhiqi Cheng, Han Wang, Yuxin Mu, Shiyi Xu, Shugong Zhou, Yongjin |
| author_facet | Ai, Zhiqi Cheng, Han Wang, Yuxin Mu, Shiyi Xu, Shugong Zhou, Yongjin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10740 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dual Data Scaling for Robust Two-Stage User-Defined Keyword Spotting Ai, Zhiqi Cheng, Han Wang, Yuxin Mu, Shiyi Xu, Shugong Zhou, Yongjin Sound 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. |
| title | Dual Data Scaling for Robust Two-Stage User-Defined Keyword Spotting |
| topic | Sound |
| url | https://arxiv.org/abs/2510.10740 |