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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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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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Cited by 2 Pith papers
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Sequence-to-Sequence Neural Diarization with Automatic Speaker Detection and Representation
A single sequence-to-sequence network with detection and representation decoders achieves state-of-the-art online and offline speaker diarization on DIHARD-II and DIHARD-III.
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Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm
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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