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arXiv · 2506.14177

Can we train ASR systems on Code-switch without real code-switch data? Case study for Singapore's languages

Tuan Nguyen·Huy-Dat Tran
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Abstract

Code-switching (CS), common in multilingual settings, presents challenges for ASR due to scarce and costly transcribed data caused by linguistic complexity. This study investigates building CS-ASR using synthetic CS data. We propose a phrase-level mixing method to generate synthetic CS data that mimics natural patterns. Utilizing monolingual augmented with synthetic phrase-mixed CS data to fine-tune large pretrained ASR models (Whisper, MMS, SeamlessM4T). This paper focuses on three under-resourced Southeast Asian language pairs: Malay-English (BM-EN), Mandarin-Malay (ZH-BM), and Tamil-English (TA-EN), establishing a new comprehensive benchmark for CS-ASR to evaluate the performance of leading ASR models. Experimental results show that the proposed training strategy enhances ASR performance on monolingual and CS tests, with BM-EN showing highest gains, then TA-EN and ZH-BM. This finding offers a cost-effective approach for CS-ASR development, benefiting research and industry.

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Tuan Nguyen, Huy-Dat Tran. 2025-06-17. Can we train ASR systems on Code-switch without real code-switch data? Case study for Singapore's languages. https://arxiv.org/abs/2506.14177

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