Neural Speaker Diarization with Speaker-Wise Chain Rule

Abstract

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.

Submitted to Interspeech 2020

References in corpus (1)

• Sequence to Sequence Learning with Neural Networks

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• Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers
• Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors
• End-to-End Speaker Diarization as Post-Processing
• Semi-Supervised Training with Pseudo-Labeling for End-to-End Neural Diarization
• End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection