A Genetic Algorithm Feature Selection Approach to Robust Classification between "Positive" and "Negative" Emotional States in Speakers

The aim of acquiring knowledge about the emotional state of a speaker is to improve the robustness of speech recognition systems, as the mechanisms producing speech vary in the presence of emotions, and also to improve the machine´s perception of a speaker´s emotional state so as to respond to his/her requests more appropriately. The paper proposes an approach based on genetic algorithms to determine a set of features that will allow robust classification of positive and negative emotional states. Starting from a vector of 414 features, a subset of features is obtained providing a good discrimination between positive and negative slates, while maintaining low computational complexity