Neural speaker diarization with speaker-wise chain rule

Yusuke Fujita, Shinji Watanabe, Shota Horiguchi, Yawen Xue, Jing Shi, Kenji Nagamatsu

Research output: Contribution to journalArticlepeer-review


Speaker diarization is an essential step for processing multi-speaker audio. Although an end-to-end neural diarization (EEND) method achieved state-of-the-art performance, it is limited to a fixed number of speakers. In this paper, we solve this fixed number of speaker issue by a novel speaker-wise conditional inference method based on the probabilistic chain rule. In the proposed method, each speaker’s speech activity is regarded as a single random variable, and is estimated sequentially conditioned on previously estimated other speakers’ speech activities. Similar to other sequence-to-sequence models, the proposed method produces a variable number of speakers with a stop sequence condition. We evaluated the proposed method on multi-speaker audio recordings of a variable number of speakers. Experimental results show that the proposed method can correctly produce diarization results with a variable number of speakers and outperforms the state-of-the-art end-to-end speaker diarization methods in terms of diarization error rate.

Original languageEnglish
JournalUnknown Journal
Publication statusPublished - 2020 Jun 2


  • Chain rule
  • End-to-end
  • Neural network
  • Speaker diarization

ASJC Scopus subject areas

  • General

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