Baseform adaptation for large vocabulary hidden Markov model based speech recognition systems

A method for adaptation of the IBM speech recognition system in the situation where the system is already trained for the new speaker and one tries to further adapt and improve the system while it is actually being used by the new speaker in the recognition mode is described. A special kind of adaptation is investigated where the emphasis is not on the adaptation of the statistical parameters of the Markov models but on the adaptation of the structure of these models. This structure is defined by the baseforms describing the composition of word models from phone models in the system. Therefore, baseform adaptation corresponds directly to the adaptation of the new system to the personal speaker characteristics of the new user. Several different baseform adaptation schemes are investigated and it is demonstrated that for a speaker who has already trained the system and achieves a 95.2% recognition performance, the performance can be further improved to 96.3%