Multi-Channel End-to-End Neural Diarization with Distributed Microphones

Shota Horiguchi; Yuki Takashima; Paola Garcia; Shinji Watanabe; Yohei Kawaguchi

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arXiv · 2110.04694

Multi-Channel End-to-End Neural Diarization with Distributed Microphones

Shota Horiguchi·Yuki Takashima·Paola Garcia·Shinji Watanabe·Yohei Kawaguchi

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Abstract

Recent progress on end-to-end neural diarization (EEND) has enabled overlap-aware speaker diarization with a single neural network. This paper proposes to enhance EEND by using multi-channel signals from distributed microphones. We replace Transformer encoders in EEND with two types of encoders that process a multi-channel input: spatio-temporal and co-attention encoders. Both are independent of the number and geometry of microphones and suitable for distributed microphone settings. We also propose a model adaptation method using only single-channel recordings. With simulated and real-recorded datasets, we demonstrated that the proposed method outperformed conventional EEND when a multi-channel input was given while maintaining comparable performance with a single-channel input. We also showed that the proposed method performed well even when spatial information is inoperative given multi-channel inputs, such as in hybrid meetings in which the utterances of multiple remote participants are played back from the same loudspeaker.

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BibTeX RIS

Shota Horiguchi, Yuki Takashima, Paola Garcia, Shinji Watanabe, Yohei Kawaguchi. 2021-10-10. Multi-Channel End-to-End Neural Diarization with Distributed Microphones. https://arxiv.org/abs/2110.04694

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