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arXiv · 2011.02160

Data Augmentation for End-to-end Code-switching Speech Recognition

Chenpeng Du·Hao Li·Yizhou Lu·Lan Wang·Yanmin Qian
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Abstract

Training a code-switching end-to-end automatic speech recognition (ASR) model normally requires a large amount of data, while code-switching data is often limited. In this paper, three novel approaches are proposed for code-switching data augmentation. Specifically, they are audio splicing with the existing code-switching data, and TTS with new code-switching texts generated by word translation or word insertion. Our experiments on 200 hours Mandarin-English code-switching dataset show that all the three proposed approaches yield significant improvements on code-switching ASR individually. Moreover, all the proposed approaches can be combined with recent popular SpecAugment, and an addition gain can be obtained. WER is significantly reduced by relative 24.0% compared to the system without any data augmentation, and still relative 13.0% gain compared to the system with only SpecAugment

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Chenpeng Du, Hao Li, Yizhou Lu, Lan Wang, Yanmin Qian. 2020-11-04. Data Augmentation for End-to-end Code-switching Speech Recognition. https://doi.org/10.1109/slt48900.2021.9383620

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