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- W4375869070 abstract "A Multilingual Keyword Spotting (KWS) system detects spoken keywords over multiple locales. Conventional monolingual KWS approaches do not scale well to multilingual scenarios because of high development/maintenance costs and lack of resource sharing. To overcome this limit, we propose two locale-conditioned universal models with locale feature concatenation and feature-wise linear modulation (FiLM). We compare these models with two baseline methods: locale-specific monolingual KWS, and a single universal model trained over all data. Experiments over 10 localized language datasets show that locale-conditioned models substantially improve accuracy over baseline methods across all locales in different noise conditions. FiLM performed the best, improving on average FRR by 61% (relative) compared to monolingual KWS models of similar sizes." @default.
- W4375869070 created "2023-05-10" @default.
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- W4375869070 date "2023-06-04" @default.
- W4375869070 modified "2023-09-27" @default.
- W4375869070 title "Locale Encoding for Scalable Multilingual Keyword Spotting Models" @default.
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- W4375869070 doi "https://doi.org/10.1109/icassp49357.2023.10095383" @default.
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