PCA Based Feature Selection Algorithm on Speaker-independent Speech Emotion Recognition
面向非特定人语音情感识别的PCA特征选择方法

A very important part of emotion recognition is how to select effective emotional features. Until now, some feature selection algorithms, which are usually used, can help boost recognition accuracy. But some defects, such as less robustness in theory, a higher randomness, more computation, still exist. For these reasons, a new feature selection algorithm based on PCA (principal component analysis) was proposed. First the original feature set was transformed by PCA, then analyzing the weights of these features using the transforming matrix and finally, choosing the important features according to their weights. hhe experiment result shows that features, which arc selected by this method, make a high contribution to the recognition accuracy and they are important.