Speaker adaptation for large vocabulary speech recognition systems using speaker Markov models

An alternative approach to speaker adaptation for a large-vocabulary hidden-Markov-model-based speech recognition system is described. The goal of this investigation was to train the IBM speech recognition system with only five minutes of speech data from a new speaker instead of the usual 20 minutes without the recognition rate dropping by more than 1-2%. The approach is based on the use of a stochastic model representing the different properties of the new speaker and an old speaker for which the full training set of 20 minutes is available. It is called a speaker Markov model. It is shown how the parameters of such a model can be derived and how it can be used for transforming the training set of the old speaker in order to use it in addition to the short training set of the new speaker. The adaptation algorithm was tested with 12 speakers. The average recognition rate dropped from 96.4% to 95.2% for a 5000-word vocabulary task. The decoding time increased by a factor of 1.35; this factor is often 3-5 if other adaptation algorithms are used