Automatic Cluster Complexity and Quantity Selection: Towards Robust Speaker Diarization

Abstract

The goal of speaker diarization is to determine where each participant speaks in a recording. One of the most commonly used technique is agglomerative clustering, where some number of initial models are grouped into the number of present speakers. The choice of complexity, topology, and the number of initial models is vital to the final outcome of the clustering algorithm. In prior systems, these parameters were directly assigned based on development data, and were the same for all recordings. In this paper we present three techniques to select the parameters individually for each case, obtaining a system that is more robust to changes in the data. Although the choice of these values depends on tunable parameters, they are less sensitive to changes in the acoustic data and to how the algorithm distributes data among the different clusters. We show that by using the three techniques, we achieve an improvement up to 8% relative in the development set and 19% relative in the test set over prior systems.