Overlap-aware End-to-End Supervised Hierarchical Graph Clustering for Speaker Diarization

• URL: http://arxiv.org/abs/2401.12850v1
• Date: Tue, 23 Jan 2024 15:35:44 GMT
• Title: Overlap-aware End-to-End Supervised Hierarchical Graph Clustering for Speaker Diarization
• Authors: Prachi Singh, Sriram Ganapathy
• Abstract summary: We propose an end-to-end supervised hierarchical clustering algorithm based on graph neural networks (GNN) The proposed E-SHARC framework improves significantly over the state-of-art diarization systems.
• Score: 41.24045486520547
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Speaker diarization, the task of segmenting an audio recording based on speaker identity, constitutes an important speech pre-processing step for several downstream applications. The conventional approach to diarization involves multiple steps of embedding extraction and clustering, which are often optimized in an isolated fashion. While end-to-end diarization systems attempt to learn a single model for the task, they are often cumbersome to train and require large supervised datasets. In this paper, we propose an end-to-end supervised hierarchical clustering algorithm based on graph neural networks (GNN), called End-to-end Supervised HierARchical Clustering (E-SHARC). The E-SHARC approach uses front-end mel-filterbank features as input and jointly learns an embedding extractor and the GNN clustering module, performing representation learning, metric learning, and clustering with end-to-end optimization. Further, with additional inputs from an external overlap detector, the E-SHARC approach is capable of predicting the speakers in the overlapping speech regions. The experimental evaluation on several benchmark datasets like AMI, VoxConverse and DISPLACE, illustrates that the proposed E-SHARC framework improves significantly over the state-of-art diarization systems.

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