Topic-Aware Dialogue Speech Recognition with Transfer Learning

Yuanfeng Song, Di Jiang, Xueyang Wu, Qian Xu, Raymond Chi-Wing Wong, Qiang Yang

Dialogue speech widely exists in scenarios such as chitchat, meeting and customer service. General-purpose speech recognition systems usually neglect the topic information in the context of dialogue speech, which has great potential for improving the performance of speech recognition. In this paper, we propose a transfer learning mechanism to conduct topic-aware recognition for dialogue speech. We first propose a new probabilistic topic model named Dialogue Speech Topic Model (DSTM) that is specialized for modeling the context of dialogue speech. We further propose a novel transfer learning mechanism for DSTM to significantly reduce its training cost while preserving its effectiveness for accurate topic inference. The experiment results demonstrate that proposed techniques in language model adaptation effectively improve the performance of the state-of-the-art Automatic Speech Recognition (ASR) system.

doi: 10.21437/Interspeech.2019-1694

Cite as: Song, Y., Jiang, D., Wu, X., Xu, Q., Wong, R.C.-W., Yang, Q. (2019) Topic-Aware Dialogue Speech Recognition with Transfer Learning. Proc. Interspeech 2019, 829-833, doi: 10.21437/Interspeech.2019-1694

@inproceedings{song19_interspeech,
  author={Yuanfeng Song and Di Jiang and Xueyang Wu and Qian Xu and Raymond Chi-Wing Wong and Qiang Yang},
  title={{Topic-Aware Dialogue Speech Recognition with Transfer Learning}},
  year=2019,
  booktitle={Proc. Interspeech 2019},
  pages={829--833},
  doi={10.21437/Interspeech.2019-1694}
}