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- W3204123830 abstract "Code-switching (CS) is common in daily conversations where more than onelanguage is used within a sentence. The difficulties of CS speech recognitionlie in alternating languages and the lack of transcribed data. Therefore, thispaper uses the recently successful self-supervised learning (SSL) methods toleverage many unlabeled speech data without CS. We show that hiddenrepresentations of SSL models offer frame-level language identity even if themodels are trained with English speech only. Jointly training CTC and languageidentification modules with self-supervised speech representations improves CSspeech recognition performance. Furthermore, using multilingual speech data forpre-training obtains the best CS speech recognition." @default.
- W3204123830 created "2021-10-11" @default.
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- W3204123830 date "2021-10-07" @default.
- W3204123830 modified "2023-09-27" @default.
- W3204123830 title "Mandarin-English Code-switching Speech Recognition with Self-supervised Speech Representation Models" @default.
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