Semi-Supervised ASR by End-to-End Self-Training

Yang Chen, Weiran Wang, Chao Wang

While deep learning based end-to-end automatic speech recognition (ASR) systems have greatly simplified modeling pipelines, they suffer from the data sparsity issue. In this work, we propose a self-training method with an end-to-end system for semi-supervised ASR. Starting from a Connectionist Temporal Classification (CTC) system trained on the supervised data, we iteratively generate pseudo-labels on a mini-batch of unsupervised utterances with the current model, and use the pseudo-labels to augment the supervised data for immediate model update. Our method retains the simplicity of end-to-end ASR systems, and can be seen as performing alternating optimization over a well-defined learning objective. We also perform empirical investigations of our method, regarding the effect of data augmentation, decoding beamsize for pseudo-label generation, and freshness of pseudo-labels. On a commonly used semi-supervised ASR setting with the Wall Street Journal (WSJ) corpus, our method gives 14.4% relative WER improvement over a carefully-trained base system with data augmentation, reducing the performance gap between the base system and the oracle system by 46%.

 DOI: 10.21437/Interspeech.2020-1280

Cite as: Chen, Y., Wang, W., Wang, C. (2020) Semi-Supervised ASR by End-to-End Self-Training. Proc. Interspeech 2020, 2787-2791, DOI: 10.21437/Interspeech.2020-1280.

  author={Yang Chen and Weiran Wang and Chao Wang},
  title={{Semi-Supervised ASR by End-to-End Self-Training}},
  booktitle={Proc. Interspeech 2020},