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The DKU-DukeECE-Lenovo System for the Diarization Task of the 2021 VoxCeleb Speaker Recognition Challenge

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arxiv 2109.02002 v2 pith:I5XC2WOU submitted 2021-09-05 eess.AS cs.SD

Weiqing Wang , Danwei Cai , Qingjian Lin , Lin Yang , Junjie Wang , Jin Wang , Ming Li This is my paper · ORCID
classification eess.AScs.SD
keywords speakerchallengedetectionmodelactivitydiarizationdifferentdku-dukeece-lenovo
verification ladder T0 review T1 audit T2 compute T3 formal
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This report describes the submission of the DKU-DukeECE-Lenovo team to the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2021 track 4. Our system including a voice activity detection (VAD) model, a speaker embedding model, two clustering-based speaker diarization systems with different similarity measurements, two different overlapped speech detection (OSD) models, and a target-speaker voice activity detection (TS-VAD) model. Our final submission, consisting of 5 independent systems, achieves a DER of 5.07% on the challenge test set.

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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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