Recognizing emotions from student speech in tutoring dialogues

We investigate the automatic classification of student emotional states in a corpus of human-human spoken tutoring dialogues. We first annotated student turns in this corpus for negative, neutral and positive emotions. We then automatically extracted acoustic and prosodic features from the student speech, and compared the results of a variety of machine learning algorithms that use 8 different feature sets to predict the annotated emotions. Our best results have an accuracy of 80.53 % and show 26.28 % relative improvement over a baseline. These results suggest that the intelligent tutoring spoken dialogue system we are developing can be enhanced to automatically predict and adapt to student emotional states.