  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.
