Odyssey 2012 - The Speaker and Language Recognition Workshop

June 25-28, 2012

Variational Bayes Logistic Regression as Regularized Fusion for NIST SRE 2010

Ville Hautamäki (1), Kong Aik Lee (2), Anthony Larcher (2), Tomi Kinnunen (1), Bin Ma (2), Haizhou Li (2)

(1) School of Computing, University of Eastern Finland (UEF), Finland
(2) Human Language Technology Department, Institute for Infocomm Research (I2R), Singapore

Fusion of the base classifiers is seen as a way to achieve high performance in state-of-the-art speaker verification systems. Typically, we are looking for base classifiers that would be complementary. We might also be interested in reinforcing good base classifiers by including others that are similar to them. In any case, the final ensemble size is typically small and has to be formed based on some rules of thumb. We are interested to find out a subset of classifiers that has a good generalization performance. We approach the problem from sparse learning point of view. We assume that the true, but unknown, fusion weights are sparse. As a practical solution, we regularize weighted logistic regression loss function by elastic-net and LASSO constraints. However, all regularization methods have an additional parameter that controls the amount of regularization employed. This needs to be separately tuned. In this work, we use variational Bayes approach to automatically obtain sparse solutions without additional cross-validation. Variational Bayes method improves the baseline method in 3 out of 4 sub-conditions. Index Terms: logistic regression, regularization, compressed sensing, linear fusion, speaker verification

Full Paper

Bibliographic reference.  Hautamäki, Ville / Lee, Kong Aik / Larcher, Anthony / Kinnunen, Tomi / Ma, Bin / Li, Haizhou (2012): "Variational Bayes logistic regression as regularized fusion for NIST SRE 2010", In Odyssey-2012, 268-274.