Voice Activity Detection Algorithm based on Improved Radial Basis Function Neural Network

Abstract

Voice activity detection (VAD) is the key of voice recognition, voice synthesis and speech-sound enhancement.For the sake of improve the accuracy and robustness of speech endpoint detection system. Combining the advantages of adaptive genetic algorithm (AGA) and improved radial basis function network (RBF) defects in existing learning methods. This paper presents a comprehensive detection method-- Adaptive genetic algorithm radial basis function network. This method uses adaptive genetic algorithm to simultaneously optimize the center, the width and the structure of RBF network. The method using wavelet analysis to extract the characteristics of the speech signal, use them as an input amount to the radial basis function networks. Establish voice detection system model, this method enhance the accuracy of the detection system and has better robustness.