Community Detection Graph Convolutional Network for Overlap-Aware Speaker Diarization
Abstract
The clustering algorithm plays a crucial role in speaker diarization systems. However, traditional clustering algorithms suffer from the complex distribution of speaker embeddings and lack of digging potential relationships between speakers in a session. We propose a novel graph-based clustering approach called Community Detection Graph Convolutional Network (CDGCN) to improve the performance of the speaker diarization system. The CDGCN-based clustering method consists of graph generation, sub-graph detection, and Graph-based Overlapped Speech Detection (Graph-OSD). Firstly, the graph generation refines the local linkages among speech segments. Secondly the sub-graph detection finds the optimal global partition of the speaker graph. Finally, we view speaker clustering for overlap-aware speaker diarization as an overlapped community detection task and design a Graph-OSD component to output overlap-aware labels. By capturing local and global information, the speaker diarization system with CDGCN clustering outperforms the traditional Clustering-based Speaker Diarization (CSD) systems on the DIHARD III corpus.
Cite
@article{arxiv.2306.14530,
title = {Community Detection Graph Convolutional Network for Overlap-Aware Speaker Diarization},
author = {Jie Wang and Zhicong Chen and Haodong Zhou and Lin Li and Qingyang Hong},
journal= {arXiv preprint arXiv:2306.14530},
year = {2023}
}
Comments
Accepted by ICASSP2023