# The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap

arXiv: https://arxiv.org/abs/2102.01363 · HTML: https://mlsanity.com/paper/2102.01363/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 28 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [pyannote.audio: neural building blocks for speaker diarization](https://mlsanity.com/paper/1911.01255/) (2019)
- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [End-to-End Neural Speaker Diarization with Self-attention](https://mlsanity.com/paper/1909.06247/) (2019)
- [End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors](https://mlsanity.com/paper/2005.09921/) (2020)
- [The Third DIHARD Diarization Challenge](https://mlsanity.com/paper/2012.01477/) (2020)
- [DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs](https://mlsanity.com/paper/2011.01997/) (2020)
- [Microsoft Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2020](https://mlsanity.com/paper/2010.11458/) (2020)
- [Neural Speaker Diarization with Speaker-Wise Chain Rule](https://mlsanity.com/paper/2006.01796/) (2020)
- [End-to-End Speaker Diarization as Post-Processing](https://mlsanity.com/paper/2012.10055/) (2020)
- [End-to-end Domain-Adversarial Voice Activity Detection](https://mlsanity.com/paper/1910.10655/) (2019)
- [BUT System for the Second DIHARD Speech Diarization Challenge](https://mlsanity.com/paper/2002.11356/) (2020)
- [DOVER: A Method for Combining Diarization Outputs](https://mlsanity.com/paper/1909.08090/) (2019)
- [BUT System Description for DIHARD Speech Diarization Challenge 2019](https://mlsanity.com/paper/1910.08847/) (2019)

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

- [Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors](https://mlsanity.com/paper/2107.01545/) (2021)
- [Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors](https://mlsanity.com/paper/2206.02432/) (2022)
- [Unifying Diarization, Separation, and ASR with Multi-Speaker Encoder](https://mlsanity.com/paper/2508.20474/) (2025)
- [Adapting Speaker Embeddings for Speaker Diarisation](https://mlsanity.com/paper/2104.02879/) (2021)
- [Royalflush Speaker Diarization System for ICASSP 2022 Multi-channel Multi-party Meeting Transcription Challenge](https://mlsanity.com/paper/2202.04814/) (2022)
- [End-to-End Integration of Speech Separation and Voice Activity Detection for Low-Latency Diarization of Telephone Conversations](https://mlsanity.com/paper/2303.12002/) (2023)
- [From Modular to End-to-End Speaker Diarization](https://mlsanity.com/paper/2407.08752/) (2024)
- [Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm](https://mlsanity.com/paper/2506.02610/) (2025)

## Abstract (arXiv)

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.