Text Independent Speaker Verification using Enhanced Sorted Gaussian Mixture Model

Abstract

In this work we study an enhanced sorting function for the recently developed sorted GMM, which is computationally efficient method for implementing the Gaussian mixture model universal background model (GMM-UBM) scheme. The sorted GMM employ partial search and thus has lower computational complexity and relaxed memory requirements when compared to the well-known tree-structured GMM of the same model order. Experimental evaluation of the sorted GMM and its enhanced version was performed on two databases: (1) clean speech in Farsi recorded from TV broadcasts, and (2) telephone quality speech in english (NIST 2002 SRE one-speaker detection data). The enhanced sorting scheme outperformed the original one, primarily for cases where very high acceleration rates were targeted, in scenarios where there was match between training and testing conditions. However, in mismatched train-test conditions the original sorted GMM performed better. Finally, the sorted GMM proved 14 times faster than the baseline system at the cost of only 0.43 increase in equal error rate.