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The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap

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arxiv 2102.01363 v1 pith:MQRNRTI4 submitted 2021-02-02 eess.AS cs.CLcs.SD

Shota Horiguchi , Nelson Yalta , Paola Garcia , Yuki Takashima , Yawen Xue , Desh Raj , Zili Huang , Yusuke Fujita
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Shinji Watanabe Sanjeev Khudanpur
This is my paper · ORCID
classification eess.AScs.CLcs.SD
keywords systemsubsystemsdiarizationchallengecompetitivecorediharddover-lap
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper provides a detailed description of the Hitachi-JHU system that was submitted to the Third DIHARD Speech Diarization Challenge. The system outputs the ensemble results of the five subsystems: two x-vector-based subsystems, two end-to-end neural diarization-based subsystems, and one hybrid subsystem. We refine each system and all five subsystems become competitive and complementary. After the DOVER-Lap based system combination, it achieved diarization error rates of 11.58 % and 14.09 % in Track 1 full and core, and 16.94 % and 20.01 % in Track 2 full and core, respectively. With their results, we won second place in all the tasks of the challenge.

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  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...

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