Minimal cross-correlation criterion for speech emotion multi-level feature selection

The problem of speech emotion recognition commonly is dealt with by delivering a huge feature set containing up to a few thousands different features. This can raise the “curse of dimensionality” problem and downgrade speech emotion classification process. In this paper we present minimal cross-correlation based formation of multi-level features for speech emotion classification. The feature set is initialized with most accurate feature and is expanded by selecting linearly independent features. This feature set formation technique was tested experimentally and compared with straightforward classification using predefined feature set. Results show superiority of our proposed technique by 5-25% for various emotion sets and classification settings.