Global Speaker Clustering towards Optimal Stopping Criterion in Binary Key Speaker Diarization

Abstract

The recently proposed speaker diarization technique based on binary keys provides a very fast alternative to state-of-the-art systems with little increase of Diarization Error Rate (DER). Although the approach shows great potential, it also presents issues, mainly in the stopping criterion. Therefore, exploring alternative clustering/stopping criterion approaches is needed. Recently some works have addressed the speaker clustering as a global optimization problem in order to tackle the intrinsic issues of the Agglomerative Hierarchical Clustering (AHC) (mainly the local-maximum-based decision making). This paper aims at adapting and applying this new framework to the binary key diarization system. In addition, an analysis of cluster purity across the AHC iterations is done using reference speaker ground-truth labels to select the purer clustering as input for the global framework. Experiments on the REPERE phase 1 test database show improvements of around 6% absolute DER compared to the baseline system output.