Detecting certainness in spoken tutorial dialogues

This study suggests that tutors respond to indications of student uncertainty differently from student certainty, and results of machine learning experiments indicate that acoustic-prosodic features can distinguish student certainness from other student states.

Abstract

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.