Classification of speech under stress using features selected by genetic algorithms

Determination of an emotional state through speech increases the amount of information associated with a speaker. It is therefore important to be able to detect and identify a speaker's emotional state or state of stress. The paper proposes an approach based on genetic algorithms to determine a set of features that will allow robust classification of emotional states. Starting from a vector of 462 features, a subset of features is obtained providing a good discrimination between neutral, angry, loud and Lombard states for the SUSAS simulated domain and between neutral and stressed states for the SUSAS actual domain.