Speaker adaptation based on Markov modeling of speakers in speaker-independent speech recognition

A speaker adaptation method for HMM (hidden Markov model) based speaker-independent speech recognition without supervising is presented. This method reduces the confusion between models, which is caused by training using large-size training data, by controlling the influences of the training samples used in HMM training by considering the similarity of speaker individuality. A Markov model and a hidden Markov model are used to represent an input speaker´s individuality. These models are compared through their entropy and /b, d, g, m, n, N/ recognition task. The results show that a hidden Markov model is more suitable than a Markov model