Improving Code-Switching ASR with Code-Mixing Guided Synthetic Speech
Yue Heng Yeo, Haoyang Li, Yizhou Peng, Shreyas Gopal, Hexin Liu, Leibny Paola Garcia-Perera, Hardik B. Sailor, Jeremy H. M. Wong, Eng Siong Chng
Abstract
Code-switch (CS) Automatic Speech Recognition (ASR) remains challenging due to limited availability of high quality CS text-speech pairs for training. Although synthetic data augmentation via Text-to-speech (TTS) has been explored, existing CS TTS approaches primarily optimise reconstruction fidelity and do not explicitly enforce language-boundary consistency, thereby limiting their effectiveness for CS ASR augmentation. This paper proposes a code-mixing guided preference-learning framework that steers synthetic speech generation toward improved code-switching fidelity using the Code Mixing Index (CMI). Experiments on the SEAME Mandarin-English conversational corpus demonstrate that the proposed method enhances the utility of synthetic data for ASR fine-tuning. Specifically, when fine-tuning Whisper Large, the proposed approach reduces Mixed Error Rate (MER) from 12.1%/17.8% to 8.9%/14.2% on the DevMAN and DevSGE sets, respectively.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.