A Speaker Independent Approach to the Classification of Emotional Vocal Expressions

The paper proposes a speaker independent procedure for classifying vocal expressions of emotion. The procedure is based on the splitting up of the emotion recognition process into two steps. In the first step, a combination of selected acoustic features is used to classify six emotions through a Bayesian Gaussian Mixture Model classifier (GMM). The two emotions that obtain the highest likelihood scores are selected for further processing in order to discriminate between them. For this purpose, a unique set of high-level acoustic features was identified using the Sequential Floating Forward Selection (SFFS) algorithm, and a GMM was used to separate between each couple of emotion. The mean classification rate is 81% with an improvement of 5% with respect to the most recent results obtained on the same database (75%).