Extending S2SND with a channel-attention module for multi-channel audio achieves an 8.09% diarization error rate, first place in the MISP 2025 speaker diarization task.
The DKU-DukeECE Diarization System for the VoxCeleb Speaker Recognition Challenge 2022
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abstract
This paper discribes the DKU-DukeECE submission to the 4th track of the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22). Our system contains a fused voice activity detection model, a clustering-based diarization model, and a target-speaker voice activity detection-based overlap detection model. Overall, the submitted system is similar to our previous year's system in VoxSRC-21. The difference is that we use a much better speaker embedding and a fused voice activity detection, which significantly improves the performance. Finally, we fuse 4 different systems using DOVER-lap and achieve 4.75 of the diarization error rate, which ranks the 1st place in track 4.
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Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge
Extending S2SND with a channel-attention module for multi-channel audio achieves an 8.09% diarization error rate, first place in the MISP 2025 speaker diarization task.