Adapting noisy speech models — Extended uncertainty decoding

Abstract

Most conventional techniques for noise adaptation assume a clean initial speech model which is adapted to a specific noise condition using adaptation data accumulated from the condition. In this paper, a different problem is considered, i.e. adapting a noisy speech model to a specific noise condition. For example, the initial noisy model may be a multi-condition model which is used to provide more accurate transcripts for the adaptation data than could be provided by a clean model, thereby obtaining a more accurate adaptation. We develop the formulation for this new problem by combining and extending maximum likelihood linear regression (MLLR), constrained MLLR (CMLLR) and uncertainty decoding techniques. We also present an implementation which has been tested on the Aurora 4 database, assuming an initial multi-condition model trained using white noise corrupted data. Significant word error rate (WER) reductions are achieved in comparison with other approaches.