Using unlabeled data to improve classification of emotional states in human computer interaction

The individual nature of physiological measurements of human affective states makes it very difficult to transfer statistical classifiers from one subject to another. In this work, we propose an approach to incorporate unlabeled data into a supervised classifier training in order to conduct an emotion classification. The key idea of the method is to conduct a density estimation of all available data (labeled and unlabeled) to create a new encoding of the problem. Based on this a supervised classifier is constructed. Further, numerical evaluations on the EmoRec II corpus are given, examining to what extent additional data can improve classification and which parameters of the density estimation are optimal.