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Mandarin-English Code-switching Speech Recognition with Self-supervised Speech Representation Models

2021-10-07 14:43:35
Liang-Hsuan Tseng, Yu-Kuan Fu, Heng-Jui Chang, Hung-yi Lee
arXiv_CL
           
Abstract

Code-switching (CS) is common in daily conversations where more than one language is used within a sentence. The difficulties of CS speech recognition lie in alternating languages and the lack of transcribed data. Therefore, this paper uses the recently successful self-supervised learning (SSL) methods to leverage many unlabeled speech data without CS. We show that hidden representations of SSL models offer frame-level language identity even if the models are trained with English speech only. Jointly training CTC and language identification modules with self-supervised speech representations improves CS speech recognition performance. Furthermore, using multilingual speech data for pre-training obtains the best CS speech recognition.

Abstract (translated)

URL

https://arxiv.org/abs/2110.03504

PDF

https://arxiv.org/pdf/2110.03504.pdf

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