Speech recognition using speaker adaptation by system parameter transformation

Presents a speaker adaptation scheme which transforms the prototype speaker´s Hidden Markov word models to those of a new speaker. Transformations are applied to both the state transition matrix and the probability distribution functions of a hidden Markov word model. These transformations are optimized through maximizing the joint probability of a set of input pronunciations of the new speaker. Details of these parameter transformations and experimental verification are presented. The test uses a 210-word vocabulary with each having a four-state Hidden Markov word model. The test speaker consists of three males and two females with one male heavily accented. By having the system retrained up to a four-minute adaptation speech, a subset of the 210-word vocabulary, the performance shows an improvement of recognition accuracy from 22.5% to 92.1%.