Reduced Feature Set for Emotion Based Spoken Utterances of Normal and Special Children Using Multivariate Analysis and Decision Trees

The current paper deals with the use of multivariate data analysis and decision tree methods in order to reduce the feature set for the normal and special children speech in four different emotions: anger, happiness, neutral and sadness. Ten features were extracted, by an algorithm implemented in a previous study to classify the speech emotions of normal and special children. In the current study, the best features are selected using multivariate analysis: principal component analysis (PCA), factor analysis and decision tree. Step by step PCA is applied to reduce the feature set according to the variables that are collinear. The obtained reduced feature sets are applicable to both normal and special children samples. Experimental results revealed that PCA yields the feature set comprising pitch, intensity, formant, LPCC and rate of acceleration. Factor analysis provides three feature sets out of which the feature set comprising of Rasta PLP, MFCC, ZCR, and intensity provides the best result. Decision tree yields a feature set comprising energy, pitch and LPCC.