Multi-Modal Data Augmentation for End-to-end ASR

Adithya Renduchintala, Shuoyang Ding, Matthew Wiesner, Shinji Watanabe

We present a new end-to-end architecture for automatic speech recognition (ASR) that can be trained using symbolic input in addition to the traditional acoustic input. This architecture utilizes two separate encoders: one for acoustic input and another for symbolic input, both sharing the attention and decoder parameters. We call this architecture a multi-modal data augmentation network (MMDA), as it can support multi-modal (acoustic and symbolic) input and enables seamless mixing of large text datasets with significantly smaller transcribed speech corpora during training. We study different ways of transforming large text corpora into a symbolic form suitable for training our MMDA network. Our best MMDA setup obtains small improvements on character error rate (CER) and as much as 7-10% relative word error rate (WER) improvement over a baseline both with and without an external language model.

 DOI: 10.21437/Interspeech.2018-2456

Cite as: Renduchintala, A., Ding, S., Wiesner, M., Watanabe, S. (2018) Multi-Modal Data Augmentation for End-to-end ASR. Proc. Interspeech 2018, 2394-2398, DOI: 10.21437/Interspeech.2018-2456.

  author={Adithya Renduchintala and Shuoyang Ding and Matthew Wiesner and Shinji Watanabe},
  title={Multi-Modal Data Augmentation for End-to-end ASR},
  booktitle={Proc. Interspeech 2018},