Supervised Hierarchical Clustering using Graph Neural Networks for Speaker Diarization

Understand

Conventional methods for speaker diarization involve windowing an audio file into short segments to extract speaker embeddings, followed by an unsupervised clustering of the embeddings.

• This multi-step approach generates speaker assignments for each segment.
• In this paper, we propose a novel Supervised HierArchical gRaph Clustering algorithm (SHARC) for speaker diarization where we introduce a hierarchical structure using Graph Neural Network (GNN) to perform supervised clustering.
• The supervision allows the model to update the representations and directly improve the clustering performance, thus enabling a single-step approach for diarization.