The paper introduced a speech feature grouping algorithm for the speech recognition system based traditional Markov in accordance with the large computation of the traditional hidden Markov model and the Viterbi algorithm as well as the Gaussian mixture distribution probability. For the speech characteristic parameters, clustering was executed by K-Means algorithm on the basis of the first and second segmentation, and then obtained the grouped characteristic parameters and the parameters to be grouped, and the speech samples can be divided into different characteristic group according to these two parameters. On this basis, a grouping training algorithm was proposed by using the redundant, which improved the accuracy of grouping the speech characteristic by clustering algorithm. Compared with the traditional HMM method, the amount of calculation can be reduced more than 60% in the case of ensuring the speech recognition rate.
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