SLiCK: Exploiting Subsequences for Length-Constrained Keyword Spotting

Kumari Nishu; Minsik Cho; Devang Naik

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arXiv · 2409.09067

SLiCK: Exploiting Subsequences for Length-Constrained Keyword Spotting

Kumari Nishu·Minsik Cho·Devang Naik

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Abstract

User-defined keyword spotting on a resource-constrained edge device is challenging. However, keywords are often bounded by a maximum keyword length, which has been largely under-leveraged in prior works. Our analysis of keyword-length distribution shows that user-defined keyword spotting can be treated as a length-constrained problem, eliminating the need for aggregation over variable text length. This leads to our proposed method for efficient keyword spotting, SLiCK (exploiting Subsequences for Length-Constrained Keyword spotting). We further introduce a subsequence-level matching scheme to learn audio-text relations at a finer granularity, thus distinguishing similar-sounding keywords more effectively through enhanced context. In SLiCK, the model is trained with a multi-task learning approach using two modules: Matcher (utterance-level matching task, novel subsequence-level matching task) and Encoder (phoneme recognition task). The proposed method improves the baseline results on Libriphrase hard dataset, increasing AUC from $88.52$ to $94.9$ and reducing EER from $18.82$ to $11.1$.

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BibTeX RIS

Kumari Nishu, Minsik Cho, Devang Naik. 2024-09-06. SLiCK: Exploiting Subsequences for Length-Constrained Keyword Spotting. https://arxiv.org/abs/2409.09067

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