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One of the best current methods for modeling dynamic speech signal is using of HMM model. The speech recognition systems based on HMM can be able to compute the best likelihood measure between unknown input pattern and reference models by using Viterbi algorithm. Whereas such algorithm is based on dynamic programming, it consists of many computations with increasing number of reference words. In this paper, we will present a new evolutionary methodology based on synergic HMM and GA that will be able to compute likelihood measurement between unknown input pattern and reference patterns in the parallel form and based on cellular automata. We introduce this algorithm as HGC. The HGC algorithm will be compared with the Viterbi algorithm from theldquorecognition accuracyrdquo and ldquorecognition speedrdquo viewpoints. Obtained results show that the HGC and Viterbi algorithms are close from ldquorecognition accuracyrdquo viewpoint, but HGCisso faster than the Viterbi.