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

2020-11-04 07:12:44
Chenpeng Du, Hao Li, Yizhou Lu, Lan Wang, Yanmin Qian
arXiv_CL
         
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

Abstract (translated)

URL

https://arxiv.org/abs/2011.02160

PDF

https://arxiv.org/pdf/2011.02160.pdf

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