8th International Conference on Spoken Language Processing

Jeju Island, Korea
October 4-8, 2004

Children's Emotion Recognition in an Intelligent Tutoring Scenario

Mark Hasegawa-Johnson, Stephen Levinson, Tong Zhang

Univ. of Illinois at Urbana-Champaign, USA

This paper presents an approach to automatically recognize emotion which children exhibit in an intelligent tutoring system. Emotion recognition can assist the computer agent to adapt its tutorial strategies to improve the efficiency of knowledge transmission. In this study, we detect three emotional classes: confidence, puzzle, and hesitation. Emotion is detected by means of lexical, prosodic, spectral, and syntactic analyses of users' speech. An automatic speech recognition system serves as the fundamental constituent of the system. A robust classification and regression tree (CART) integrates the various information sources together for final decision. The effectiveness of the proposed approach has been tested on data collected by Wizard-of-Oz (WoZ) experiments. Our emotion recognition was speaker-independent, and yielded 91.3% accuracy. The test results showed that the spectral and duration-related prosodic features played very important roles in emotion recognition.

Full Paper

Bibliographic reference.  Hasegawa-Johnson, Mark / Levinson, Stephen / Zhang, Tong (2004): "Children's emotion recognition in an intelligent tutoring scenario", In INTERSPEECH-2004, 1441-1444.