Feature adaptation using projection of Gaussian posteriors

Abstract

In this paper we consider the use of non-linear methods for feature adaptation to reduce the mismatch between test and training conditions. The non-linearity is introduced by using the posteriors of a set of Gaussians to adapt the original features. Parameters are estimated to maximize the likelihood of the test data. The modeling framework used is based on the fMPE models [1]. We observe significant gains (17% relative) on a test data base recorded in a car. We also see significant gains on top of FMLLR (38% relative over the baseline and 8.5% relative over FMLLR).