Speaker adaptation using an eigenphone basis

We describe a new method of estimating speaker-dependent hidden Markov models for speakers in a closed population. Our method differs from previous approaches in that it is based on an explicit model of the correlations between all of the speakers in the population, the idea being that if there is not enough data to estimate a Gaussian mean vector for a given speaker then data from other speakers can be used provided that we know how the speakers are correlated with each other. We explain how to estimate inter-speaker correlations using a Kullback-Leibler divergence minimization technique which can be applied to the problem of estimating the parameters of all of the hyperdistributions that are currently used in Bayesian speaker adaptation.