  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.
