Speech Emotion Recognition by Using Combinations of C5.0, Neural Network (NN), and Support Vector Machines (SVM) Classification Methods

Speech is the fastest and most natural method for human to communicate. This has led several researches to be done in the field of the interaction effects between human and machine. Hence, it is necessary to design machines which can intelligently recognize the emotion of a human voice. However, we are still far from having a natural interaction between the human and machine because machines cannot distinguish the emotion of the speaker. This has established a new field in the literature, namely the speech emotion recognition systems. The accuracy of these systems depends on various factors such as the number and type of the emotion manners as well as the feature selection and the classifier sort. In this paper, classification methods of the Neural Network (NN), Support Vector Machine (SVM), the combination of NN and SVM (NN-SVM), NN and SVM (NN-SVM), NN and C5.0 (NN-C5.0), C5.0 and SVM (SVM-C5.0), and finally the combination of NN, SVM, and C5.0 (NN-SVM-C5.0) have been verified, and their efficiencies in speech emotion recognition have been compared. The utilized features in this research include energy, power, Zero Crossing Rate (ZCR), pitch, and Mel-scale Frequency Cepstral Coefficients (MFCC). The presented results in this paper demonstrate that using the proposed NN-C5.0 classification method is more efficient in recognizing the emotion states-to the extent of 6%- to 30% depending on the number of emotions states-than SVM, NN, and other aforementioned combinations of classification methods.