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The DKU-DukeECE Diarization System for the VoxCeleb Speaker Recognition Challenge 2022

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arxiv 2210.01677 v1 pith:2CQQE7BT submitted 2022-10-04 eess.AS

Weiqing Wang , Xiaoyi Qin , Ming Cheng , Yucong Zhang , Kangyue Wang , Ming Li This is my paper · ORCID
classification eess.AS
keywords systemactivitydetectiondiarizationmodelspeakervoicechallenge
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge

    eess.AS 2025-05 conditional novelty 4.0 of 10

    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.

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