mlsanityData to 14 Oct 2026

Third DIHARD Challenge Evaluation Plan

arXiv 2006.05815 ↗First posted 4 Jun 2020eess.AS cs.SDcited by 8

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Abstract (arXiv)

This paper introduces the third DIHARD challenge, the third 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 two tracks evaluating diarization performance when starting from a reference speech segmentation (track 1) and diarization from raw audio scratch (track 2). We describe the task, metrics, datasets, and evaluation protocol.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Jun 2020.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 0; 2024: 2.

8 papers.

  1. 2021A Review of Speaker Diarization: Recent Advances with Deep Learningcited by 124
  2. 2024Sortformer: A Novel Approach for Permutation-Resolved Speaker Supervision in Speech-to-Text Systemscited by 15
  3. 2022Multi-scale Speaker Diarization with Dynamic Scale Weightingcited by 11
  4. 2021Three-class Overlapped Speech Detection using a Convolutional Recurrent Neural Networkcited by 8
  5. 2024Assessing the Robustness of Spectral Clustering for Deep Speaker Diarizationcited by 2
  6. 2022Late Audio-Visual Fusion for In-The-Wild Speaker Diarizationcited by 1
  7. 2021Domain-Dependent Speaker Diarization for the Third DIHARD Challengecited by 0
  8. 2022Collar-aware Training for Streaming Speaker Change Detection in Broadcast Speechcited by 0