  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. In the proposed work, the input segment
embeddings are treated as nodes of a graph with the edge weights corresponding
to the similarity scores between the nodes. We also propose an approach to
jointly update the embedding extractor and the GNN model to perform end-to-end
speaker diarization (E2E-SHARC). During inference, the hierarchical clustering
is performed using node densities and edge existence probabilities to merge the
segments until convergence. In the diarization experiments, we illustrate that
the proposed E2E-SHARC approach achieves 53% and 44% relative improvements over
the baseline systems on benchmark datasets like AMI and Voxconverse,
respectively.
