Yuanfeng Song
Di Jiang
Weiwei Zha
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 topicaware 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.