An improved cluster model selection method for agglomerative hierarchical speaker clustering using incremental Gaussian mixture models

In this paper, we improve our previous cluster model selection method for agglomerative hierarchical speaker clustering (AHSC) based on incremental Gaussian mixture models (iGMMs). In the previous work, we measured the likelihood of all the data points in a given cluster for each mixture component of the GMM modeling the cluster. Then, we selected the N -best component Gaussians with the highest likelihoods to make the GMM refined for the purpose of better cluster representation. N was chosen empirically then, but it is difficult to set an optimal N universally in general. In this work, we propose an improved method to adaptively select component Gaussians from the GMM considered, by measuring the degree of representativeness of each Gaussian component, which we define in this paper. Experiments on two data sets including 17 meeting speech excerpts verify that the proposed approach improves the overall clustering performance by approximately 20% and 10% (relative), respectively, compared to the previous method.