关键词: 小波分析, 神经网络, 语音端点, 粒子群优化算法, 特征选择

Abstract: In order to improve detection rate of the speech endpoint, this paper proposes a speech endpoint detection method based on BP neural network optimized by improved momentum particle swarm optimization algorithm. The features of speech signals are extracted by wavelet analysis, then the features are input to BP neural network to build the speech endpoints detection model in which the BP neural network’s parameters are optimized by particle swarm optimization algorithm, the simulation experiments are carried out on Matlab environments. The experimental results show that the proposed method improves the detection rate, and reduces the false detection rate and false negative rate effectively, WA-IMPSO-BP is a high detection rate and strong resistant noise performance speech detection algorithm.

Key words: wavelet analysis, neural network, speech endpoints, particle swarm optimization algorithm, feature selection