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arXiv · 2412.20805

Phoneme-Level Contrastive Learning for User-Defined Keyword Spotting with Flexible Enrollment

Li Kewei·Zhou Hengshun·Shen Kai·Dai Yusheng·Du Jun
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Abstract

User-defined keyword spotting (KWS) enhances the user experience by allowing individuals to customize keywords. However, in open-vocabulary scenarios, most existing methods commonly suffer from high false alarm rates with confusable words and are limited to either audio-only or text-only enrollment. Therefore, in this paper, we first explore the model's robustness against confusable words. Specifically, we propose Phoneme-Level Contrastive Learning (PLCL), which refines and aligns query and source feature representations at the phoneme level. This method enhances the model's disambiguation capability through fine-grained positive and negative comparisons for more accurate alignment, and it is generalizable to jointly optimize both audio-text and audio-audio matching, adapting to various enrollment modes. Furthermore, we maintain a context-agnostic phoneme memory bank to construct confusable negatives for data augmentation. Based on this, a third-category discriminator is specifically designed to distinguish hard negatives. Overall, we develop a robust and flexible KWS system, supporting different modality enrollment methods within a unified framework. Verified on the LibriPhrase dataset, the proposed approach achieves state-of-the-art performance.

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Li Kewei, Zhou Hengshun, Shen Kai, Dai Yusheng, Du Jun. 2024-12-30. Phoneme-Level Contrastive Learning for User-Defined Keyword Spotting with Flexible Enrollment. https://arxiv.org/abs/2412.20805

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