# Reformulating Speaker Diarization as Community Detection With Emphasis On Topological Structure

arXiv: https://arxiv.org/abs/2204.12112 · HTML: https://mlsanity.com/paper/2204.12112/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 26 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [End-to-End Neural Speaker Diarization with Permutation-Free Objectives](https://mlsanity.com/paper/1909.05952/) (2019)
- [End-to-End Neural Speaker Diarization with Self-attention](https://mlsanity.com/paper/1909.06247/) (2019)
- [Speaker Diarization with LSTM](https://mlsanity.com/paper/1710.10468/) (2017)
- [End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors](https://mlsanity.com/paper/2005.09921/) (2020)
- [Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap](https://mlsanity.com/paper/2003.02405/) (2020)
- [Integrating end-to-end neural and clustering-based diarization: Getting the best of both worlds](https://mlsanity.com/paper/2010.13366/) (2020)
- [Advances in integration of end-to-end neural and clustering-based diarization for real conversational speech](https://mlsanity.com/paper/2105.09040/) (2021)
- [LSTM based Similarity Measurement with Spectral Clustering for Speaker Diarization](https://mlsanity.com/paper/1907.10393/) (2019)
- [End-to-End Speaker Diarization as Post-Processing](https://mlsanity.com/paper/2012.10055/) (2020)
- [A Real-time Speaker Diarization System Based on Spatial Spectrum](https://mlsanity.com/paper/2107.09321/) (2021)

## Built on by (most cited first, 3 of 3)

- [Exploring Speaker-Related Information in Spoken Language Understanding for Better Speaker Diarization](https://mlsanity.com/paper/2305.12927/) (2023)
- [Contrastive Deep Nonnegative Matrix Factorization for Community Detection](https://mlsanity.com/paper/2311.02357/) (2023)
- [Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm](https://mlsanity.com/paper/2506.02610/) (2025)

## Abstract (arXiv)

Clustering-based speaker diarization has stood firm as one of the major approaches in reality, despite recent development in end-to-end diarization. However, clustering methods have not been explored extensively for speaker diarization. Commonly-used methods such as k-means, spectral clustering, and agglomerative hierarchical clustering only take into account properties such as proximity and relative densities. In this paper we propose to view clustering-based diarization as a community detection problem. By doing so the topological structure is considered. This work has four major contributions. First it is shown that Leiden community detection algorithm significantly outperforms the previous methods on the clustering of speaker-segments. Second, we propose to use uniform manifold approximation to reduce dimension while retaining global and local topological structure. Third, a masked filtering approach is introduced to extract "clean" speaker embeddings. Finally, the community structure is applied to an end-to-end post-processing network to obtain diarization results. The final system presents a relative DER reduction of up to 70 percent. The breakdown contribution of each component is analyzed.