Speaker Diarization with Lexical Information

Tae Jin Park, Kyu J. Han, Jing Huang, Xiaodong He, Bowen Zhou, Panayiotis Georgiou, Shrikanth Narayanan

This work presents a novel approach for speaker diarization to leverage lexical information provided by automatic speech recognition. We propose a speaker diarization system that can incorporate word-level speaker turn probabilities with speaker embeddings into a speaker clustering process to improve the overall diarization accuracy. To integrate lexical and acoustic information in a comprehensive way during clustering, we introduce an adjacency matrix integration for spectral clustering. Since words and word boundary information for word-level speaker turn probability estimation are provided by a speech recognition system, our proposed method works without any human intervention for manual transcriptions. We show that the proposed method improves diarization performance on various evaluation datasets compared to the baseline diarization system using acoustic information only in speaker embeddings.

 DOI: 10.21437/Interspeech.2019-1947

Cite as: Park, T.J., Han, K.J., Huang, J., He, X., Zhou, B., Georgiou, P., Narayanan, S. (2019) Speaker Diarization with Lexical Information. Proc. Interspeech 2019, 391-395, DOI: 10.21437/Interspeech.2019-1947.

  author={Tae Jin Park and Kyu J. Han and Jing Huang and Xiaodong He and Bowen Zhou and Panayiotis Georgiou and Shrikanth Narayanan},
  title={{Speaker Diarization with Lexical Information}},
  booktitle={Proc. Interspeech 2019},