Date Added: Jun 2012
Automatic emotion recognition in speech is a current research area with a wide range of applications in human-machine interactions. This paper uses the Support Vector Machine (SVM), to classify five emotional states: anger, happiness, sadness, surprise and a neutral state. The classification performance of the selected feature subset was done with that of the Mel Frequency Cepstrum Coefficients (MFCC), Periodicity Histogram and Fluctuation Pattern. Within the method based on SVM, a new method by using Multi-class SVM is used as a classifier. Experiments were conducted on the Danish Emotion Speech (DES) Database.