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.

通过图神经网络的端到端监督分层聚类算法（E-SHARC）可以改善目前状态下的说话人辨别系统，特别是在重叠语音区域的预测上。