Speaker dependent speech emotion recognition using MFCC and Support Vector Machine

In human computer interaction, speech emotion recognition is playing a pivotal part in the field of research. Human emotions consist of being angry, happy, sad, disgust, neutral. In this paper the features are extracted with hybrid of pitch, formants, zero crossing, MFCC and its statistical parameters. The pitch detection is done by cepstral algorithm after comparing it with autocorrelation and AMDF. The training and testing part of the SVM classifier is compared with different kernel function like linear, polynomial, quadratic and RBF. The polish database is used for the classification. The comparison between the different kernels is obtained for the corresponding feature vector.