Title :
Semi-continuous hidden Markov models in isolated word recognition
Author :
Huang, X.D. ; Jack, M.A.
Author_Institution :
Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
Abstract :
A semicontinuous hidden Markov model is proposed to incorporate the vector quantization distortion into the general hidden Markov model methodology under a probabilistic framework. It provides a relatively simple but powerful tool for modeling time-varying signal sources. Experimental results show that the recognition accuracy of the semi-continuous model is measurably improved in comparison to that of the conventional discrete hidden Markov model and template-based dynamic time warping techniques
Keywords :
Markov processes; computerised pattern recognition; speech recognition; hidden Markov model; isolated word recognition; probabilistic framework; semi-continuous model; time-varying signal sources; vector quantization distortion; Bridges; Hidden Markov models; Isolation technology; Maximum likelihood estimation; Mutual information; Parameter estimation; Probability density function; Speech recognition; Training data; Vector quantization;
Conference_Titel :
Pattern Recognition, 1988., 9th International Conference on
Conference_Location :
Rome
Print_ISBN :
0-8186-0878-1
DOI :
10.1109/ICPR.1988.28254
