Multistyle classification of speech under stress using feature subset selection based on genetic algorithms

The 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. Various techniques are used in the literature to classify emotional/stressed states on the basis of speech, often using different speech feature vectors at the same time. This study proposes a new feature vector that will allow better classification of emotional/stressed states. The components of the feature vector are obtained from a feature subset selection procedure based on genetic algorithms. A good discrimination between neutral, angry, loud and Lombard states for the simulated domain of the Speech Under Simulated and Actual Stress (SUSAS) database and between neutral and stressed states for the actual domain of the SUSAS database is obtained.