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USTC-NELSLIP System Description for DIHARD-III Challenge

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arxiv 2103.10661 v1 pith:Y4577UMR submitted 2021-03-19 cs.SD cs.LGeess.AS

Yuxuan Wang , Maokui He , Shutong Niu , Lei Sun , Tian Gao , Xin Fang , Jia Pan , Jun Du
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Chin-Hui Lee
This is my paper · ORCID
classification cs.SDcs.LGeess.AS
keywords systemchallengedescriptiondiarizationprocessingspeechtrackachieved
verification ladder T0 review T1 audit T2 compute T3 formal
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This system description describes our submission system to the Third DIHARD Speech Diarization Challenge. Besides the traditional clustering based system, the innovation of our system lies in the combination of various front-end techniques to solve the diarization problem, including speech separation and target-speaker based voice activity detection (TS-VAD), combined with iterative data purification. We also adopted audio domain classification to design domain-dependent processing. Finally, we performed post processing to do system fusion and selection. Our best system achieved DERs of 11.30% in track 1 and 16.78% in track 2 on evaluation set, respectively.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm

    cs.SD 2025-06 conditional novelty 4.0 of 10

    Graph attention refinement plus overlapping label propagation yields a reported 15.94% DER on DIHARD-III without oracle VAD, though internal configuration inconsistencies and missing error bars make the SOTA claim pro...

  2. 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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