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arXiv · 2109.02047

The ByteDance Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2021

Keke Wang·Xudong Mao·Hao Wu·Chen Ding·Chuxiang Shang·Rui Xia·Yuxuan Wang
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Abstract

This paper describes the ByteDance speaker diarization system for the fourth track of the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC-21). The VoxSRC-21 provides both the dev set and test set of VoxConverse for use in validation and a standalone test set for evaluation. We first collect the duration and signal-to-noise ratio (SNR) of all audio and find that the distribution of the VoxConverse's test set and the VoxSRC-21's test set is more closer. Our system consists of voice active detection (VAD), speaker embedding extraction, spectral clustering followed by a re-clustering step based on agglomerative hierarchical clustering (AHC) and overlapped speech detection and handling. Finally, we integrate systems with different time scales using DOVER-Lap. Our best system achieves 5.15\% of the diarization error rate (DER) on evaluation set, ranking the second at the diarization track of the challenge.

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Keke Wang, Xudong Mao, Hao Wu, Chen Ding, Chuxiang Shang, Rui Xia, Yuxuan Wang. 2021-09-05. The ByteDance Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2021. https://arxiv.org/abs/2109.02047

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