# Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm

arXiv: https://arxiv.org/abs/2506.02610 · HTML: https://mlsanity.com/paper/2506.02610/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 18 of 18)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [Powerset multi-class cross entropy loss for neural speaker diarization](https://mlsanity.com/paper/2310.13025/) (2023)
- [End-to-end speaker segmentation for overlap-aware resegmentation](https://mlsanity.com/paper/2104.04045/) (2021)
- [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)
- [The Third DIHARD Diarization Challenge](https://mlsanity.com/paper/2012.01477/) (2020)
- [Encoder-Decoder Based Attractors for End-to-End Neural Diarization](https://mlsanity.com/paper/2106.10654/) (2021)
- [Neural Speaker Diarization with Speaker-Wise Chain Rule](https://mlsanity.com/paper/2006.01796/) (2020)
- [Speaker diarization with session-level speaker embedding refinement using graph neural networks](https://mlsanity.com/paper/2005.11371/) (2020)
- [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)
- [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)
- [Target-Speaker Voice Activity Detection via Sequence-to-Sequence Prediction](https://mlsanity.com/paper/2210.16127/) (2022)
- [DiaPer: End-to-End Neural Diarization with Perceiver-Based Attractors](https://mlsanity.com/paper/2312.04324/) (2023)
- [Reformulating Speaker Diarization as Community Detection With Emphasis On Topological Structure](https://mlsanity.com/paper/2204.12112/) (2022)
- [EEND-M2F: Masked-attention mask transformers for speaker diarization](https://mlsanity.com/paper/2401.12600/) (2024)
- [Community Detection Graph Convolutional Network for Overlap-Aware Speaker Diarization](https://mlsanity.com/paper/2306.14530/) (2023)

## Abstract (arXiv)

In speaker diarization, traditional clustering-based methods remain widely used in real-world applications. However, these methods struggle with the complex distribution of speaker embeddings and overlapping speech segments. To address these limitations, we propose an Overlapping Community Detection method based on Graph Attention networks and the Label Propagation Algorithm (OCDGALP). The proposed framework comprises two key components: (1) a graph attention network that refines speaker embeddings and node connections by aggregating information from neighboring nodes, and (2) a label propagation algorithm that assigns multiple community labels to each node, enabling simultaneous clustering and overlapping community detection. Experimental results show that the proposed method significantly reduces the Diarization Error Rate (DER), achieving a state-of-the-art 15.94% DER on the DIHARD-III dataset without oracle Voice Activity Detection (VAD), and an impressive 11.07% with oracle VAD.