• Corpus ID: 219179559

Neural Speaker Diarization with Speaker-Wise Chain Rule

@article{Fujita2020NeuralSD,
  title={Neural Speaker Diarization with Speaker-Wise Chain Rule},
  author={Yusuke Fujita and Shinji Watanabe and Shota Horiguchi and Yawen Xue and Jing Shi and Kenji Nagamatsu},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.01796}
}
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… 
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The proposed SGSD system can significantly improve the performance of state-of-the-art CSD systems, yielding relative diarization error rate reductions of 20.2 % and 20.8 % on the development set and evaluation set, respectively.
Encoder-Decoder Based Attractor Calculation for End-to-End Neural Diarization
TLDR
This paper introduces encoder-decoder-based attractor calculation module (EDA) to EEND and proposes a method that aligns the estimated diarization results with the results of an external speech activity detector, which enables fair comparison against pipeline approaches.
Semi-Supervised Training with Pseudo-Labeling for End-to-End Neural Diarization
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