Dynamic adaptation of hidden Markov model for robust speech recognition

An algorithm is presented for adaptation and self-learning of the hidden Markov model (HMM). It makes the HMM-based speech recognition robust, so that well-trained models can be adapted to new speaking conditions or a new speaker. The self-learning consists of the fact that, during recognition, all test tokens can be used to augment the current model. Both procedures increase the size of the training set. The algorithm was tested on a speaker-dependent speech recognition system for the whole Chinese vocabulary and a speaker-independent system for 0-9 digits. Experiments show that the algorithm is very successful, both for new-speaker adaptation and for variations of speech in a single speaker under various conditions