Implementation and Optimization of a Speech Recognition System Based on Hidden Markov ModelUsing Genetic Algorithm

In this paper, a speech recognition system with isolated words is implemented. Discrete hidden Markov model is used to recognize words. Feature vector consists of cepstral and deltacepstrum coefficients which are extracted from speech signal frames. Since the discrete Markov model is used, the featurevector is mapped to a discrete element by a vector quantizer. One of the problems we face in training of Markov model is thatthe classical training method could obtain locally optimal solution. To overcome this problem we have used genetic algorithm to get globally optimal solution. Experimental resultsshow that this hybrid speech recognition obtains better performance than traditional method.

Keywords:

speech recognition , hidden Markov model , feature vector , vector quantization , genetic algorithm