Modeling improvement of the continuous hidden Markov model for speech recognition

The authors improve the modeling of the conventional continuous hidden Markov model (HMM) for speech recognition in two aspects. One is to use new functions to model the phonetic duration distribution. These functions can well approximate the unsymmetrical distribution and have relatively simple forms for computation. The other aspect is to use a proportional coefficient to adjust dimensional effects of the output density functions and the phonetic duration functions in the HMM. Using these new techniques, the authors got 7.8% improvement of the recognition correct rate in the vowel recognition experiments of the continuous speech