INTERSPEECH 2006 - ICSLP
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.
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.