SLiCK: Exploiting Subsequences for Length-Constrained Keyword Spotting

• URL: http://arxiv.org/abs/2409.09067v1
• Date: Fri, 6 Sep 2024 01:08:29 GMT
• Title: SLiCK: Exploiting Subsequences for Length-Constrained Keyword Spotting
• Authors: Kumari Nishu, Minsik Cho, Devang Naik,
• Abstract summary: Keywords are often bounded by a maximum keyword length, which has been largely under-leveraged in prior works. We introduce a subsequence-level matching scheme to learn audio-text relations at a finer granularity. The proposed method improves the baseline results on hard dataset, increasing AUC from $88.52$ to $94.9$ and reducing EER from $18.82 to $11.1$.
• Score: 5.697227044927832
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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