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Collaborative Research: Monitoring Student State in Tutorial Spoken Dialogue Diane Litman Computer Science Department and Learning Research and Development Center University of Pittsburgh
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Project Participants Post-docs –Kate Forbes-Riley, Joel Tetreault Graduate Students –Mihai Rotaru (PhD), Beatriz Maeiereizo-Tokeshi (MS) Undergraduate Student (REU) –Gregory Nicholas Programmer –Scott Silliman Columbia University –Julia Hirschberg, Jennifer Venditti (Post-doc), and Jackson Liscombe (PhD student)
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Infrastructure System Design and Implementation –ITSPOKE (Intelligent Tutoring SPOKEn dialogue system) Version 1 and 2 Corpus Collection – 128 human-human dialogues – 395 human-computer dialogues Annotation – Positive/negative/neutral (pilot study) – Certainty and frustration/anger
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Emotion Prediction Extraction of acoustic/prosodic, lexical, and other turn-level features of student turns Addition of word versus breath-group features Classification using supervised and semi- supervised machine learning techniques
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Adaptive Dialogue Systems Interactions with speech recognition performance Correlation with student learning and user satisfaction Mining tutor responses to student emotional states
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Remaining Year (No-Cost Extension Planned) Incorporate User State Predictor into ITSPOKE Experimentally evaluate original vs. affective ITSPOKE
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Journal Publications Diane J. Litman and Kate Forbes-Riley. Recognizing Student Emotions and Attitudes on the Basis of Utterances in Spoken Tutoring Dialogues with both Human and Computer Tutors. Speech Communication, in press.
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Conference Proceedings (2004, 2005) Kate Forbes-Riley and Diane Litman. Predicting Emotion in Spoken Dialogue from Multiple Knowledge Sources. In Proceedings of the Human Language Technology Conference: 4th Meeting of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL 2004). Diane J. Litman and Kate Forbes-Riley, Predicting Student Emotions in Computer-Human Tutoring Dialogues. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004). Diane Litman. Correlating Student Acoustic-Prosodic Profiles with Student Learning in Spoken Tutoring Dialogues. In Proceedings 9th European Conference on Speech Communication and Technology (Interspeech-2005/Eurospeech). Mihai Rotaru and Diane J. Litman. Using Word-level Pitch Features to Better Predict Student Emotions during Spoken Tutoring Dialogues. In Proceedings 9th European Conference on Speech Communication and Technology (Interspeech-2005/Eurospeech). Mihai Rotaru, Diane J. Litman, and Katherine Forbes-Riley. Interactions between Speech Recognition Problems and User Emotions. In Proceedings 9th European Conference on Speech Communication and Technology (Interspeech-2005/Eurospeech).
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Conference Proceedings (2006) Kate Forbes-Riley and Diane Litman. Modelling User Satisfaction and Student Learning in a Spoken Dialogue Tutoring System with Generic, Tutoring, and User Affect Parameters. Proceedings of the Human Language Technology/North American Association for Computational Linguistics (HLT/NAACL). Joel Tetreault and Diane Litman. Comparing the Utility of State Features in Spoken Dialogue Using Reinforcement Learning. Proceedings of the Human Language Technology/North American Association for Computational Linguistics (HLT/NAACL ). Joel Tetreault and Diane Litman. Using Reinforcement Learning to Build a Better Model of Dialogue State. Proceedings 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL).
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Workshop and Companion Proceedings Diane J. Litman and Kate Forbes-Riley. Annotating Student Emotional States in Spoken Tutoring Dialogues. In Proceedings of 5th SIGdial Workshop on Discourse and Dialogue (SIGdial), 2004. Diane J. Litman and Scott Silliman. ITSPOKE: An Intelligent Tutoring Spoken Dialogue System. In Proceedings of the Human Language Technology Conference: 4th Meeting of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL) (Companion Proceedings), 2004. Beatriz Maeireizo, Diane Litman, and Rebecca Hwa, Co-training for Predicting Emotions with Spoken Dialogue Data. In Companion Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL), Barcelona, 2004. Kate Forbes-Riley and Diane J. Litman. Using Bigrams to Identify Relationships Between Student Certainness States and Tutor Responses in a Spoken Dialogue Corpus. In Proceedings of 6th SIGdial Workshop on Discourse and Dialogue (SIGdial), 2005.
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