Research Of Speech Recognition Based On Neural Networks

Language is one of the most important means of exchanging information among the mankind. With the continuous developing and application of electronic computer and the increasing improvement of artificial intelligent, people expect to make the computer recognize human language. This requirement makes the technique of speech recognition have immense space to develop. Up to now, most speech recognition is based on conventional linear system theory, such as Dynamic Time Warping (DTW) and Hidden Markov Model (HMM). With the deep study of speech recognition, it is found that speech signal is a complex nonlinear process. If the study of speech recognition wants to break through, nonlinear system theory method must be introduced to it. Recently, with the development of nonlinear-system theories such as artificial neural networks (ANN), it is possible to apply these theories to speech recognition.This paper focuses on thorough analyses and investigations of the main process of speech recognition. In the stage of drawing the character, the usually used character parameters LPCC and MFCC and so on, are described in this paper. This paper mainly studies speech recognition based on BP network. In order to represent the feature of speech better and avoid the localization of using subsection linear method, the method for speech recognition based on mixed parameter of MFCC and LPCC is also proposed. Experimental results show that using mixed feature parameters achieves better effects than using MFCC feature parameters.