Detecting certainness in spoken tutorial dialogues

What role does affect play in spoken tutorial systems and is it automatically detectable? We investigated the classification of student certainness in a corpus collected for ITSPOKE, a speech-enabled Intelligent Tutorial System (ITS). Our study suggests that tutors respond to indications of student uncertainty differently from student certainty. Results of machine learning experiments indicate that acoustic-prosodic features can distinguish student certainness from other student states. A combination of acoustic-prosodic features extracted at two levels of intonational analysis --- breath groups and turns --- achieves 76.42% classification accuracy, a 15.8% relative improvement over baseline performance. Our results suggest that student certainness can be automatically detected and utilized to create better spoke dialog ITSs.