Adaptation experiments on the spine database with the extended maximum likelihood linear transformation (EMLLT) model

Abstract

This paper applies the recently proposed Extended Maximum Likelihood Linear Transformation (EMLLT) model for inverse covariances in a Speaker Adaptive Training (SAT) context. The paper adapts standard algorithms for maximum likelihood estimation of linear transforms for mean, variance and feature space adaptation respectively, to the EMLLT model. Experimental results showing word-error-rate improvements are reported on the SPINE2 database. The system described here is the best-performing system submitted by IBM in the SPINE2 evaluation conducted by NIST in October 2001.