  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
