Improving Code-Switching and Named Entity Recognition in ASR with Speech
  Editing based Data Augmentation
Zheng Liang Zheshu Song Ziyang Ma Chenpeng Du Kai Yu Xie Chen
Recently, end-to-end (E2E) automatic speech recognition (ASR) models have
made great strides and exhibit excellent performance in general speech
recognition. However, there remain several challenging scenarios that E2E
models are not competent in, such as code-switching and named entity
recognition (NER). Data augmentation is a common and effective practice for
these two scenarios. However, the current data augmentation methods mainly rely
on audio splicing and text-to-speech (TTS) models, which might result in
discontinuous, unrealistic, and less diversified speech. To mitigate these
potential issues, we propose a novel data augmentation method by applying the
text-based speech editing model. The augmented speech from speech editing
systems is more coherent and diversified, also more akin to real speech. The
experimental results on code-switching and NER tasks show that our proposed
method can significantly outperform the audio splicing and neural TTS based
data augmentation systems.