Work

Using system and user performance features to improve emotion detection in spoken tutoring dialogs

Year: 2006

Type: conference-paper

Abstract: In this study, we incorporate automatically obtained system/user performance features into machine learning experiments to detect student emotion in computer tutoring dialogs. Our results show a relative improvement of 2.7% on classification accuracy and 8.08% on Kappa over using standard lexical, prosodie, sequential, and identification features. This level of improvement is comparable to the performance improvement shown in previous studies by applying dialog acts or lexical/prosodic-/discourse- level contextual features.

Source: Interspeech (USB)

DOI: 10.21437/interspeech.2006-274

Authors: Hua Ai, Diane J. Litman, Kate Forbes-Riley, Mihai Rotaru, Joel Tetreault, Amruta Purandare

Institution: University of Pittsburgh

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: green

Funder: National Science Foundation

Awards: Collaborative Research: Monitoring Student State in Tutorial Spoken Dialogue, ITR : Tutoring scientific explanations via natural language dialogue