Abstract

Abstract. Currently, a lot of research in the field of intelligent tutoring systems is concerned with recognising student's emotions and affects. The recognition is done by extracting features from information sources like speech, typing and mouse clicking behaviour or physiological sensors. In former work we proposed some low-level speech features for perceived task difficulty recognition in intelligent tutoring systems. However, by extracting these features some information hidden in the speech input is loosed. Hence, in this paper we propose and investigate speech and pause histograms as features, which preserve some of the loosed information. The approach of using speech and pause histograms for perceived task difficulty recognition is evaluated by experiments on data collected in a study with German students solving mathematical tasks.