Towards emotion prediction in spoken tutoring dialogues

Diane Litman, Kate Forbes, Scott Silliman
2003 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology companion volume of the Proceedings of HLT-NAACL 2003--short papers - NAACL '03   unpublished
Human tutors detect and respond to student emotional states, but current machine tutors do not. Our preliminary machine learning experiments involving transcription, emotion annotation and automatic feature extraction from our human-human spoken tutoring corpus indicate that the spoken tutoring system we are developing can be enhanced to automatically predict and adapt to student emotional states.
doi:10.3115/1073483.1073501 fatcat:evpma226mjhqri5qal45up6dga