Maximum likelihood eigenspace and MLLR for speech recognition in noisy environments

A technique for rapid speaker adaptation,called eigenvoices, was introduced recently. The key idea is to confine models in a very low-dimensional linear vector space. This space summarizes a priori knowledge that we have about speaker models. In many practical systems, however, there is a mismatch between the conditions in which the training data were collected and test conditions: prior knowledge becomes improper. Furthermore, prior statistics or models of this mismatch may not be available. We expose two key results: first, we use a maximum-likelihood estimator of prior information in matched conditions, called MLES, leading to an improvement of adaptation by a relative 14%, and second, we show how we can apply a blind scheme for learning noise, MLLR, achieving an additional 7,7% relative improvement in noisy conditions.