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.

本文介绍了 Microsoft 公司的单声道多人对话录音扬声器辨识系统，并用 VoxCeleb 挑战赛 2020 年的说话人辨识赛道进行了评估。论文首先介绍了解决真实多人对话记录中的问题的系统设计。然后介绍了组件的细节，其中包括 Res2Net 基于说话人嵌入提取器，基于 conformer 的连续语音分离和泄漏过滤，以及修改后的 DOVER 方法用于系统融合。使用 VoxSRC challenge 2020 提供的数据集进行系统评估。我们的最佳系统在开发集上的辨识错误率（DER）为 3.71％，在评估集上的 DER 为 6.23％，并在挑战的辨识赛道上排名第一。