  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.
