Speaker adaptation techniques for speech recognition using probabilistic models

In speech recognition, speaker adaptation refers to the range of techniques whereby a speech recognition system is adapted to the acoustic features of a specific user using a small sample of utterances from that user. In recent years the practical development of speaker-independent speech recognition systems using continuous density hidden Markov models has seen significant progress; however, the recognition performance of these systems has not yet reached that of speaker-dependent speech recognition systems in which a user's speech is registered beforehand. Much hope has therefore been placed on the establishment of speaker adaptation techniques that can bring performance of a speaker-independent system Lip to that of a speaker-dependent one using the smallest amounts of data. In this paper we present a survey of previous research into speaker adaptation techniques focusing particularly on three important approaches in this area: maximum a posteriori (MAP) parameter estimation, maximum likelihood linear regression (MLLR), and eigenvoices. We also discuss approaches that combine these techniques in a lateral fashion. (C) 2005 Wiley Periodicals, Inc.