This paper describes the DKU-MSXF submission to track 4 of the VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC-23). Our system pipeline contains voice activity detection, clustering-based diarization, overlapped speech detection, and target-speaker voice activity detection, where each procedure has a fused output from 3 sub-models. Finally, we fuse different clustering-based and TSVAD-based diarization systems using DOVER-Lap and achieve the 4.30% diarization error rate (DER), which ranks first place on track 4 of the challenge leaderboard.
@article{arxiv.2308.07595,
title = {The DKU-MSXF Diarization System for the VoxCeleb Speaker Recognition Challenge 2023},
author = {Ming Cheng and Weiqing Wang and Xiaoyi Qin and Yuke Lin and Ning Jiang and Guoqing Zhao and Ming Li},
journal= {arXiv preprint arXiv:2308.07595},
year = {2023}
}