Bayesian discriminative adaptation for speech recognition

Linear transform-based speaker adaptation is a standard part of many speech recognition systems. For unsupervised adaptation maximum likelihood estimation is typically used, as discriminative transforms are more heavily biased towards the supervision hypothesis which may contain errors. In this work, a Bayesian framework for discriminative adaptation is investigated. This reduces the hypothesis bias and allows robust estimates even with a limited amount of data. Various forms of discriminative maximum-a-posteriori estimation, and associated issues, are detailed. To address these problems, the use of discriminative mapping transforms is also described. The proposed framework is evaluated on an English conversational speech task.