# Third DIHARD Challenge Evaluation Plan

arXiv: https://arxiv.org/abs/2006.05815 · HTML: https://mlsanity.com/paper/2006.05815/

## Key facts

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

## Built on by (most cited first, 8 of 8)

- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [Sortformer: A Novel Approach for Permutation-Resolved Speaker Supervision in Speech-to-Text Systems](https://mlsanity.com/paper/2409.06656/) (2024)
- [Multi-scale Speaker Diarization with Dynamic Scale Weighting](https://mlsanity.com/paper/2203.15974/) (2022)
- [Three-class Overlapped Speech Detection using a Convolutional Recurrent Neural Network](https://mlsanity.com/paper/2104.02878/) (2021)
- [Assessing the Robustness of Spectral Clustering for Deep Speaker Diarization](https://mlsanity.com/paper/2403.14286/) (2024)
- [Late Audio-Visual Fusion for In-The-Wild Speaker Diarization](https://mlsanity.com/paper/2211.01299/) (2022)
- [Domain-Dependent Speaker Diarization for the Third DIHARD Challenge](https://mlsanity.com/paper/2101.09884/) (2021)
- [Collar-aware Training for Streaming Speaker Change Detection in Broadcast Speech](https://mlsanity.com/paper/2205.07086/) (2022)

## 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.