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Microsoft Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2020

2020-10-22 05:46:15
Xiong Xiao, Naoyuki Kanda, Zhuo Chen, Tianyan Zhou, Takuya Yoshioka, Yong Zhao, Gang Liu, Jian Wu, Jinyu Li, Yifan Gong
arXiv_SD
       
Abstract

This paper describes the Microsoft speaker diarization system for monaural multi-talker recordings in the wild, evaluated at the diarization track of the VoxCeleb Speaker Recognition Challenge(VoxSRC) 2020. We will first explain our system design to address issues in handling real multi-talker recordings. We then present the details of the components, which include Res2Net-based speaker embedding extractor, conformer-based continuous speech separation with leakage filtering, and a modified DOVER (short for Diarization Output Voting Error Reduction) method for system fusion. We evaluate the systems with the data set provided by VoxSRCchallenge 2020, which contains real-life multi-talker audio collected from YouTube. Our best system achieves 3.71% and 6.23% of the diarization error rate (DER) on development set and evaluation set, respectively, being ranked the 1st at the diarization track of the challenge.

Abstract (translated)

URL

https://arxiv.org/abs/2010.11458

PDF

https://arxiv.org/pdf/2010.11458.pdf

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