Improving Emotion Recognition Performance by Random-Forest-Based Feature Selection

As technical systems around us aim at a more natural interaction, the task of automatic emotion recognition from speech receives an ever growing attention. One important question still remains unresolved: The definition of the most suitable features across different data types. In the present paper, we employed a random-forest based feature selection known from other research fields in order to select the most important features for three benchmark datasets. Investigating feature selection on the same corpus as well as across corpora, we achieved an increase in performance using only 40 to 60% of the features of the well-known emobase feature set.