Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors

TL;DR

This paper introduces EEND-GLA, a novel neural diarization method that combines local attractor-based clustering with global unsupervised clustering, enabling online and offline diarization of an unlimited number of speakers.

Contribution

EEND-GLA extends existing neural diarization by incorporating unsupervised clustering, allowing for unlimited speaker count handling in both online and offline scenarios.

Findings

Successfully diarized an unseen number of speakers.

Performed well in both online and offline inferences.

Outperformed previous methods in speaker diarization accuracy.

Abstract

A method to perform offline and online speaker diarization for an unlimited number of speakers is described in this paper. End-to-end neural diarization (EEND) has achieved overlap-aware speaker diarization by formulating it as a multi-label classification problem. It has also been extended for a flexible number of speakers by introducing speaker-wise attractors. However, the output number of speakers of attractor-based EEND is empirically capped; it cannot deal with cases where the number of speakers appearing during inference is higher than that during training because its speaker counting is trained in a fully supervised manner. Our method, EEND-GLA, solves this problem by introducing unsupervised clustering into attractor-based EEND. In the method, the input audio is first divided into short blocks, then attractor-based diarization is performed for each block, and finally, the results of each block are clustered on the basis of the similarity between locally-calculated attractors. While the number of output speakers is limited within each block, the total number of speakers estimated for the entire input can be higher than the limitation. To use EEND-GLA in an online manner, our method also extends the speaker-tracing buffer, which was originally proposed to enable online inference of conventional EEND. We introduce a block-wise buffer update to make the speaker-tracing buffer compatible with EEND-GLA. Finally, to improve online diarization, our method improves the buffer update method and revisits the variable chunk-size training of EEND. The experimental results demonstrate that EEND-GLA can perform speaker diarization of an unseen number of speakers in both offline and online inferences.