Most current speech recognition systems are based on Hidden Markov Model (HMM) using Viterbi search in decoding stage. Whereas such algorithm is in terms of dynamic programming, it consists of many computations with increasing number of reference words. This paper presents a novel method called HGSCA computing likelihood measurement between an unknown input pattern and a reference model based on a synergy between HMM and Genetic Algorithm (GA) in a parallel form by Stochastic Cellular Automata (SCA). The HGSCA algorithm is compared with the Viterbi algorithm from the “recognition time” and “recognition error” view points. Experimental results show although HGSCA and Viterbi algorithms are very close in recognition accuracy, the HGSCA is so faster than the Viterbi algorithm.