摘要：In this paper, we present a new hybrid approach for isolated spoken word recognition using Hidden Markov Model models (HMM) combined with Dynamic time warping (DTW). HMM have been shown to be robust in spoken recognition systems. We propose to extend the HMM method by combining it with the DTW algorithm in order to combine the advantages of these two powerful pattern recognition technique. In this work we do a comparative evaluation between traditional Continuous Hidden Markov Models (GHMM), and the new approach DTW/GHMM. This approach integrates the prototype (word reference template) for each word in the training phase of the Hybrid system. An iterative algorithm based on conventional DTW algorithm and on an averaging technique is used for determined the best prototype during the training phase in order to increase model discrimination. The test phase is identical for the GHMM and DTW/GHMM methods. We evaluate the performance of each system using several different test sets and observe that, the new approach models presented the best results in all cases
关键词：speech recognition; hidden Markov models; dynamic time warping; hybrid system
