Transfer Learning of Language-independent End-to-end ASR with Language Model Fusion

Hirofumi Inaguma; Jaejin Cho; Murali Karthick Baskar; Tatsuya Kawahara; Shinji Watanabe

ICASSP 2019accepted0 citations

Transfer Learning of Language-independent End-to-end ASR with Language Model Fusion

Hirofumi Inaguma, Jaejin Cho, Murali Karthick Baskar, Tatsuya Kawahara, Shinji Watanabe

Abstract

This work explores better adaptation methods to low-resource languages using an external language model (LM) under the framework of transfer learning. We first build a language-independent ASR system in a unified sequence-to-sequence (S2S) architecture with a shared vocabulary among all languages. During adaptation, we perform LM fusion transfer, where an external LM is integrated into the decoder network of the attention-based S2S model in the whole adaptation stage, to effectively incorporate linguistic context of the target language. We also investigate various seed models for transfer learning. Experimental evaluations using the IARPA BABEL data set show that LM fusion transfer improves performances on all target five languages compared with simple transfer learning when the external text data is available. Our final system drastically reduces the performance gap from the hybrid systems.

BibTeX

@inproceedings{icassp2019_transferlearning,
  title = {Transfer Learning of Language-independent End-to-end ASR with Language Model Fusion},
  author = {Hirofumi Inaguma and Jaejin Cho and Murali Karthick Baskar and Tatsuya Kawahara and Shinji Watanabe},
  booktitle = {ICASSP 2019},
  year = {2019}
}