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

Hua, A and Litman, DJ and Forbes-Riley, K and Rotaru, M and Tetreault, J and Purandare, A (2006) Using system and user performance features to improve emotion detection in spoken tutoring dialogs. In: UNSPECIFIED.

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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.

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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Hua, A
Litman, DJdlitman@pitt.eduDLITMAN
Forbes-Riley, K
Rotaru, M
Tetreault, J
Purandare, A
Centers: University Centers > Learning Research and Development Center (LRDC)
Date: 1 January 2006
Date Type: Publication
Journal or Publication Title: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume: 2
Page Range: 797 - 800
Event Type: Conference
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Dietrich School of Arts and Sciences > Intelligent Systems
Refereed: Yes
ISBN: 9781604234497
Related URLs:
Date Deposited: 10 Oct 2014 19:12
Last Modified: 02 Feb 2019 15:59
URI: http://d-scholarship.pitt.edu/id/eprint/23214

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