Analysis of high-level features for vocal emotion recognition

The paper deals with the vocal emotion recognition which is a very important task for several applications in the field of human-machine interaction. There is a plenty of algorithms proposed up to date for this purpose that exploit different types of features and classifiers. Our previous work showed that high-level features perform very well in terms of emotion classification from speech. However, little attention has been paid so far to the statistical analysis of these features. For this reason the presented paper mainly focuses on the emotion recognition by using only high-level features. Two different emotional speech corpora were exploited in our experiments, namely the Berlin Database of Emotional Speech and the COST2102 Italian Database of Emotional Speech. Results showed that the best high-level features in terms of high discriminative power strongly differ among the databases considered on the first hand and among the emotions within each database on the second hand.