Emotion recognition using novel speech signal features

Automatic Emotion Recognition (AER) is a very recent research topic in the Human-Computer Interaction (HCI) field which still has much room to grow. In this contribution a set of novel acoustic features and Least Square-Support Vector Machines (LS-SVMs) are proposed to set up a speaker-independent Automatic Human Emotion Recognition system. Six discrete emotional states are classified throughout this work: happiness, sadness, anger, surprise, fear, and disgust. Different multi-class SVM methods are implemented in order to get the best result. The result achieved by LS-SVM is then compared by that of a Linear Classifier. We achieved an overall accuracy of 81.3%.