Bayesian adaptation in speech recognition

In order to achieve state-of-the-art performance in a speaker-dependent speech recognition task, it is necessary to collect a large number of acoustic data samples during the training process. Providing these samples to the system can be a long and tedious process for users. One way to attack this problem is to make use of extra information from a data bank representing a large population of speakers. In this paper we demonstrate that by using Bayesian techniques, prior knowledge derived from speaker-independent data can be combined with speaker-dependent training data to improve system performance.