Neural Speaker Diarization with Speaker-Wise Chain Rule

By Yusuke Fujita, Shinji Watanabe, Shota Horiguchi, Yawen Xue, Jing Shi, Kenji Nagamatsu. 2020

51 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Encoder-Decoder Based Attractor Calculation for End-to-End Neural Diarization (2021, methodology): “However, as discussed in Section II-B, it requires knowing the maximum number of speakers in advance, and it has been already veriﬁed that such a strategy results in poor performance (see [15]).”
A Review of Speaker Diarization: Recent Advances with Deep Learning (2021, unknown): “[170] proposed another approach to output the speech activity one after another by using a conditional speaker chain rule.”
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals (2020, background): “Similar ideas have also been proposed in time-domain speech separation [39] and speaker diarization [10].”
Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors (2021, methodology): “As comparison methods, the state-of-the-art x-vector-based method (VBx) [2] and several EEND-based methods that can deal with a ﬂexible number of speakers [48, 10, 14, 27] were adopted.”