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End-to-end speaker diarization approaches have shown exceptional performanceover the traditional modular approaches. To further improve the performance ofthe end-to-end speaker diarization for real speech recordings, recently workshave been proposed which integrate unsupervised clustering algorithms with theend-to-end neural diarization models. However, these methods have a number ofdrawbacks: 1) The unsupervised clustering algorithms cannot leverage thesupervision from the available datasets; 2) The K-means-based unsupervisedalgorithms that are explored often suffer from the constraint violationproblem; 3) There is unavoidable mismatch between the supervised training andthe unsupervised inference. In this paper, a robust generic neural clusteringapproach is proposed that can be integrated with any chunk-level predictor toaccomplish a fully supervised end-to-end speaker diarization model. Also, byleveraging the sequence modelling ability of a recurrent neural network, theproposed neural clustering approach can dynamically estimate the number ofspeakers during inference. Experimental show that when integrating anattractor-based chunk-level predictor, the proposed neural clustering approachcan yield better Diarization Error Rate (DER) than the constrainedK-means-based clustering approaches under the mismatched conditions.

Robust End-to-end Speaker Diarization with Generic Neural Clustering