Agglomerative Hierarchical Speaker Clustering using Incremental Gaussian Mixture Cluster Modeling

This paper proposes a novel cluster modeling method for inter-cluster distance measurement within the framework of agglomerative hierarchical speaker clustering, namely, incremental Gaussian mixture cluster modeling. This method uses a single Gaussian distribution to model each initial cluster, but represents any newly merged cluster using a distribution whose pdf is the weighted sum of the pdf's of the respective model distributions for the clusters involved in the particular merging process. As a result, clusters are smoothly transitioned to be modeled by Gaussian mixtures whose components are incremented as merging recursions continue during clustering. The proposed method can overcome the limited cluster representation capability of conventional single Gaussian cluster modeling. Through experiments on various sets of initial clusters, it is demonstrated that our approach consequently improves the reliability of speaker clustering performance.