Information Theoretic Expectation Maximization Based Gaussian Mixture Modeling for Speaker Verification

The expectation maximization (EM) algorithm is widely used in the Gaussian mixture model (GMM) as the state-of-art statistical modeling technique. Like the classical EM method, the proposed EM-Information Theoretic algorithm (EM-IT) adapts means, covariances and weights, however this process is not conducted directly on feature vectors but on a smaller set of centroids derived by the information theoretic procedure, which simultaneously minimizes the divergence between the Parzen estimates of the feature vector´s distribution within a given Gaussian component and the centroid´s distribution within the same Gaussian component. The EM-IT algorithm was applied to the speaker verification problem using NIST 2004 speech corpus and the MFCC with dynamic features. The results showed an improvement of the equal error rate (ERR) by 1.5% over the classical EM approach. The EM-IT also showed higher convergence rates compare to the EM method.