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arXiv · 2302.12961

Locale Encoding For Scalable Multilingual Keyword Spotting Models

Pai Zhu·Hyun Jin Park·Alex Park·Angelo Scorza Scarpati·Ignacio Lopez Moreno
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Abstract

A Multilingual Keyword Spotting (KWS) system detects spokenkeywords over multiple locales. Conventional monolingual KWSapproaches do not scale well to multilingual scenarios because ofhigh development/maintenance costs and lack of resource sharing.To overcome this limit, we propose two locale-conditioned universalmodels with locale feature concatenation and feature-wise linearmodulation (FiLM). We compare these models with two baselinemethods: locale-specific monolingual KWS, and a single universalmodel trained over all data. Experiments over 10 localized languagedatasets show that locale-conditioned models substantially improveaccuracy over baseline methods across all locales in different noiseconditions.FiLMperformed the best, improving on average FRRby 61% (relative) compared to monolingual KWS models of similarsizes.

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Pai Zhu, Hyun Jin Park, Alex Park, Angelo Scorza Scarpati, Ignacio Lopez Moreno. 2023-02-25. Locale Encoding For Scalable Multilingual Keyword Spotting Models. https://arxiv.org/abs/2302.12961

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