Design of Speech Recognition Classifier Based on Genetic Wavelet Neural Network
基于遗传小波神经网络的语音识别分类器设计

Classification is an important problem in speech recognition, due to the fact that the learning effects of wavelet neural network strongly depend on the number of hidden nodes, the initial weights(including thresholds) , the scale and displacement factors, the learning rate and momentum factor, which leads to weak global search capability, easily falling into local minimum values,low convergence rate, and even not convergent Genetic Algorithm (GA) has height parallel performance, random and adaptive search performance, and it has obvious advantages in solving complex and nonlinear problem. Therefore,we can combine neural network and genetic algorithm by using GA to select initial value,and use wavelet neural network to finish the learning. The simulation results show that the new model effectively improves speech recognition rate, shortens the recognition time, realizes double wins in efficient and time, establishes the foundation for practicality of the algorithm.