mlsanityData to 7 Oct 2026

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

arXiv 2110.03504 ↗First posted 7 Oct 2021cs.CL eess.AScited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 24 of its 34 references that are in the map. First posted on arXiv 7 Oct 2021.

Builds on

24 of 34 references.

References · 24

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12 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 2; 2025: 1; 2026: 2.

9 papers.