Children's emotion recognition in an intelligent tutoring scenario

Tong Zhang, Mark Hasegawa-Johnson, Stephen E. Levinson

Research output: Contribution to conferencePaperpeer-review


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.

Original languageEnglish (US)
Number of pages4
StatePublished - 2004
Event8th International Conference on Spoken Language Processing, ICSLP 2004 - Jeju, Jeju Island, Korea, Republic of
Duration: Oct 4 2004Oct 8 2004


Other8th International Conference on Spoken Language Processing, ICSLP 2004
Country/TerritoryKorea, Republic of
CityJeju, Jeju Island

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language


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