Variational Bayesian Model Selection for GMM-Speaker Verification Using Universal Background Model

In this paper we propose to use Variational Bayesian Analysis (VBA) instead of Maximum Likelihood (ML) estimation for Universal Background Model (UBM) building in GMM text independent speaker verification systems. Using VBA estimation solves the problem of the optimal choice of the UBM mixture dimensionality for the training data set, as well as the problem of noise Gaussians which are typical for ML estimation. Experiments using the NIST 2006 and 2008 SRE datasets (cellular channels only) demonstrate superior efficiency of baseline verification systems with a UBM trained using the VBA method compared to standard ML training. Verification error was reduced by almost 8%, compared to a baseline system with standard ML training for the UBM.