Spoken Emotion Classification Using ToBI Features and GMM

This study investigated the usefulness of ToBI marks in determining the emotional state conveyed in speech. The Gaussian mixture model GMM used was as the classifier structure. A total of three different classification systems were developed based on the use of three different feature vectors. They were: (a) the classical approach that used signal pitch and energy features; (b) a ToBI-only feature based on tone and break tiers; and (c) a system that used the features of both (a) and (b). In ToBI, tone tier elements were automatically determined using pitch information. Three emotional states were investigated: happiness, anger, and sadness. The overall success rate achieved for the combined system was between 75% and 100%. This work indicated that the ToBI features alone were very useful for the classification of emotion, and detection improves when classical features are used in conjunction with ToBI.