Hierarchical emotion classification using genetic algorithms

Emotion classification from speech signal is an interesting subject of machine learning applications that can provide the emotional or psychological states from speakers. This implicit information is helpful for machine to understand human behavior in more comprehensive way. Many feature extraction and classification methods have being proposed to find the most accurate and efficient method, but this is still an open question for researchers. In this paper, we propose a novel method to select features and classify emotions in hierarchical way using genetic algorithm and support vector machine classifiers in order to find the most accurate binary classification tree. We show the efficiency and robustness of our method by applying and analyzing on Berlin dataset of emotional speech and the experiment results show that our method achieves high accuracy and efficiency.