Speech Recognition Algorithm Based On Neural Network Research

Along with modern science and the development of computer technology, human and machine information exchange more and more attention, and speech recognition as a way of man-machine communication has broad application prospects, voice recognition technology has also become a modern computer technology research and development One of the important areas. Speech signal as a result of the diversity and complexity, the current efficiency of speech recognition is not high. Thus the development of efficient models and algorithms become the voice of speech recognition research an important topic. Person's pronunciation is actually a complex non-linear process, based on linear system theory of the limitations of speech recognition method shows up gradually. Voice recognition technology to make a breakthrough, it is necessary to introduce the method of non-linear theory. In recent years, artificial neural network (ANN), such as research and application of nonlinear theory of the gradual deepening of the voice recognition application of these theories possible. In this paper, neural network technology for speech recognition system used to conduct the studies.This article first voice signals in some of the basic principles, the effective extraction of characteristic parameters of three - LPC coefficients, LPC cepstrum coefficients and Mel Frequency Cepstral Coefficients (MFCC); The second describes the speech recognition model of pattern-matching and training of technical; again discussed in the speech recognition neural network application, an analysis of the model based on neural network training and recognition algorithms, the traditional neural network learning algorithm and the improved algorithm of BP neural network analysis, and suggest improvements After the neural network in speech recognition applications, network structure, and finally through the analysis of simulation experiments, that the use of MFCC and LPCC as the characteristic parameters of mixing parameters in several feature parameters than other higher recognition rate, so the optimized BP neural network voice identification system to improve performance, but also shorten the training time for the future embedded systems migrate to lay a good foundation.