关键词: 情感特征, 情感识别, 神经网络, 主成分分析

Abstract: Speech emotion information has nonlinear, redundancy and high dimension characteristics, the data has lots of noise, the traditional methods cannot eliminate the redundancy information and noise, so speech emotion recognition accuracy is quite low. In order to improve the accuracy of speech emotion recognition, this paper puts forward a speech emotion recognition model based on process neural networks strong nonlinear processing ability and wavelet analysisdenoising. The noise of speech signal is eliminated by wavelet analysis, the redundancy information is eliminated by the principal components analysis, the speech emotional signal is recognized by the process neural networks. Simulation results show that the average recognition rate of the process neural networks is higher than K neighbor by 13%, and higher than the support vector machine by 8.75%, therefore the proposed model is an effective speech emotion recognition tool.

Key words: motional feature, emotion recognition, neural network, Principal Component Analysis（PCA）