Ninth International Conference on Spoken Language Processing

Pittsburgh, PA, USA
September 17-21, 2006

Using System and User Performance Features to Improve Emotion Detection in Spoken Tutoring Dialogs

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

University of Pittsburgh, USA

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, prosodic, 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.

Full Paper

Bibliographic reference.  Ai, Hua / Litman, Diane J. / Forbes-Riley, Kate / Rotaru, Mihai / Tetreault, Joel / Purandare, Amruta (2006): "Using system and user performance features to improve emotion detection in spoken tutoring dialogs", In INTERSPEECH-2006, paper 1682-Tue1A3O.2.