Abstract
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%.
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