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A Multilingual Keyword Spotting (KWS) system detects spokenkeywords overmultiple locales. Conventional monolingual KWSapproaches do not scale well tomultilingual scenarios because ofhigh development/maintenance costs and lack ofresource sharing.To overcome this limit, we propose two locale-conditioneduniversalmodels with locale feature concatenation and feature-wiselinearmodulation (FiLM). We compare these models with two baselinemethods:locale-specific monolingual KWS, and a single universalmodel trained over alldata. Experiments over 10 localized languagedatasets show thatlocale-conditioned models substantially improveaccuracy over baseline methodsacross all locales in different noiseconditions.FiLMperformed the best,improving on average FRRby 61% (relative) compared to monolingual KWS models ofsimilarsizes.

Locale Encoding For Scalable Multilingual Keyword Spotting Models