# DOVER: A Method for Combining Diarization Outputs

arXiv: https://arxiv.org/abs/1909.08090 · HTML: https://mlsanity.com/paper/1909.08090/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 6 of 6)

- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [DiarizationLM: Speaker Diarization Post-Processing with Large Language Models](https://mlsanity.com/paper/2401.03506/) (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)
- [Multi-Channel End-to-End Neural Diarization with Distributed Microphones](https://mlsanity.com/paper/2110.04694/) (2021)
- [Probabilistic Fusion and Calibration of Neural Speaker Diarization Models](https://mlsanity.com/paper/2511.22696/) (2025)
- [Training-Free Affinity Fusion of Neural and Embedding-Based Speaker Diarization](https://mlsanity.com/paper/2609.39162/) (2026)

## Abstract (arXiv)

Speech recognition and other natural language tasks have long benefited from voting-based algorithms as a method to aggregate outputs from several systems to achieve a higher accuracy than any of the individual systems. Diarization, the task of segmenting an audio stream into speaker-homogeneous and co-indexed regions, has so far not seen the benefit of this strategy because the structure of the task does not lend itself to a simple voting approach. This paper presents DOVER (diarization output voting error reduction), an algorithm for weighted voting among diarization hypotheses, in the spirit of the ROVER algorithm for combining speech recognition hypotheses. We evaluate the algorithm for diarization of meeting recordings with multiple microphones, and find that it consistently reduces diarization error rate over the average of results from individual channels, and often improves on the single best channel chosen by an oracle.