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The Second DIHARD Diarization Challenge: Dataset, task, and baselines

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arxiv 1906.07839 v1 pith:SWFUY65C submitted 2019-06-18 eess.AS cs.CL

classification eess.AScs.CL
keywords diarizationchallengeconditionsspeechsecondconversationaldiharddomain
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper introduces the second DIHARD challenge, the second in a series of speaker diarization challenges intended to improve the robustness of diarization systems to variation in recording equipment, noise conditions, and conversational domain. The challenge comprises four tracks evaluating diarization performance under two input conditions (single channel vs. multi-channel) and two segmentation conditions (diarization from a reference speech segmentation vs. diarization from scratch). In order to prevent participants from overtuning to a particular combination of recording conditions and conversational domain, recordings are drawn from a variety of sources ranging from read audiobooks to meeting speech, to child language acquisition recordings, to dinner parties, to web video. We describe the task and metrics, challenge design, datasets, and baseline systems for speech enhancement, speech activity detection, and diarization.

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  1. M3SD: Multi-modal, Multi-scenario and Multi-language Speaker Diarization Dataset

    eess.AS 2025-06 reject novelty 5.0 of 10

    Release of M3SD, a 770+ hour pseudo-labeled multi-scenario, multi-language audio-visual speaker diarization dataset, built from YouTube and Bilibili videos without manual annotation.

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