Speaker adaptation by maximum likelihood linear regression with application to computer aided learning

This paper presents an implementation of the acoustic adaptation of continuous density hidden Markov models using Maximum Likelihood Linear Regression(MLLR). We present a possible solution for the problem of updating Gaussians that are shared across multiple states. We use a tree-based partitioning of the different mixture components in regression classes. Evaluation is done on the CoGeN (Corpus Gesproken Nederlands ) dataset and on a dyslexia reading diagnosis test for children (logopaedic data).