# The Third DIHARD Diarization Challenge

arXiv: https://arxiv.org/abs/2012.01477 · HTML: https://mlsanity.com/paper/2012.01477/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2020.

## Built on by (most cited first, 30 of 49)

- [Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers using End-to-End Speaker-Attributed ASR](https://mlsanity.com/paper/2110.03151/) (2021)
- [Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors](https://mlsanity.com/paper/2107.01545/) (2021)
- [The timing bottleneck: Why timing and overlap are mission-critical for conversational user interfaces, speech recognition and dialogue systems](https://mlsanity.com/paper/2307.15493/) (2023)
- [Generating Data with Text-to-Speech and Large-Language Models for Conversational Speech Recognition](https://mlsanity.com/paper/2408.09215/) (2024)
- [The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap](https://mlsanity.com/paper/2102.01363/) (2021)
- [Three-class Overlapped Speech Detection using a Convolutional Recurrent Neural Network](https://mlsanity.com/paper/2104.02878/) (2021)
- [Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors](https://mlsanity.com/paper/2206.02432/) (2022)
- [USTC-NELSLIP System Description for DIHARD-III Challenge](https://mlsanity.com/paper/2103.10661/) (2021)
- [Multi-scale speaker embedding-based graph attention networks for speaker diarisation](https://mlsanity.com/paper/2110.03361/) (2021)
- [Supervised Hierarchical Clustering using Graph Neural Networks for Speaker Diarization](https://mlsanity.com/paper/2302.12716/) (2023)
- [Efficient and Robust Speaker Diarization via Structured Pruning of Self-Supervised Models](https://mlsanity.com/paper/2506.18623/) (2025)
- [Overlapped speech and gender detection with WavLM pre-trained features](https://mlsanity.com/paper/2209.04167/) (2022)
- [DIVE: End-to-end Speech Diarization via Iterative Speaker Embedding](https://mlsanity.com/paper/2105.13802/) (2021)
- [In search of strong embedding extractors for speaker diarisation](https://mlsanity.com/paper/2210.14682/) (2022)
- [Adapting Speaker Embeddings for Speaker Diarisation](https://mlsanity.com/paper/2104.02879/) (2021)
- [Large-scale learning of generalised representations for speaker recognition](https://mlsanity.com/paper/2210.10985/) (2022)
- [Summary of the DISPLACE Challenge 2023 - DIarization of SPeaker and LAnguage in Conversational Environments](https://mlsanity.com/paper/2311.12564/) (2023)
- [Mamba-based Segmentation Model for Speaker Diarization](https://mlsanity.com/paper/2410.06459/) (2024)
- [BER: Balanced Error Rate For Speaker Diarization](https://mlsanity.com/paper/2211.04304/) (2022)
- [Neural Diarization with Non-autoregressive Intermediate Attractors](https://mlsanity.com/paper/2303.06806/) (2023)
- [DiaCorrect: Error Correction Back-end For Speaker Diarization](https://mlsanity.com/paper/2309.08377/) (2023)
- [Neural Speaker Diarization Using Memory-Aware Multi-Speaker Embedding with Sequence-to-Sequence Architecture](https://mlsanity.com/paper/2309.09180/) (2023)
- [Self-Tuning Spectral Clustering for Speaker Diarization](https://mlsanity.com/paper/2410.00023/) (2024)
- [SDBench: A Comprehensive Benchmark Suite for Speaker Diarization](https://mlsanity.com/paper/2507.16136/) (2025)
- [Assessing the Robustness of Spectral Clustering for Deep Speaker Diarization](https://mlsanity.com/paper/2403.14286/) (2024)
- [Shennong: a Python toolbox for audio speech features extraction](https://mlsanity.com/paper/2112.05555/) (2021)
- [TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge](https://mlsanity.com/paper/2210.14653/) (2022)
- [High-resolution embedding extractor for speaker diarisation](https://mlsanity.com/paper/2211.04060/) (2022)
- [An Explainable Proxy Model for Multiabel Audio Segmentation](https://mlsanity.com/paper/2401.08268/) (2024)
- [End-to-End Supervised Hierarchical Graph Clustering for Speaker Diarization](https://mlsanity.com/paper/2401.12850/) (2024)

## Abstract (arXiv)

DIHARD III was the third in a series of speaker diarization challenges intended to improve the robustness of diarization systems to variability in recording equipment, noise conditions, and conversational domain. Speaker diarization was evaluated under two speech activity conditions (diarization from a reference speech activity vs. diarization from scratch) and 11 diverse domains. The domains span a range of recording conditions and interaction types, including read audio-books, meeting speech, clinical interviews, web videos, and, for the first time, conversational telephone speech. A total of 30 organizations (forming 21teams) from industry and academia submitted 499 valid system outputs. The evaluation results indicate that speaker diarization has improved markedly since DIHARD I, particularly for two-party interactions, but that for many domains (e.g., web video) the problem remains far from solved.