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Computational Linguistics
Affective computing
Improvising linguistic style: social and affective bases for agent personality
AGENTS '97 Proceedings of the first international conference on Autonomous agents
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Emotion recognition from text using semantic labels and separable mixture models
ACM Transactions on Asian Language Information Processing (TALIP)
Hidden Markov model-based speech emotion recognition
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Ensemble methods for spoken emotion recognition in call-centres
Speech Communication
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Neurocomputing
Inter-coder agreement for computational linguistics
Computational Linguistics
Extracting social meaning: identifying interactional style in spoken conversation
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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Image and Vision Computing
Using linguistic cues for the automatic recognition of personality in conversation and text
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Speech Communication
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ACM Transactions on Intelligent Systems and Technology (TIST)
IEEE Transactions on Affective Computing
Large scale personality classification of bloggers
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Modeling the Temporal Evolution of Acoustic Parameters for Speech Emotion Recognition
IEEE Transactions on Affective Computing
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Error Weighted Semi-Coupled Hidden Markov Model for Audio-Visual Emotion Recognition
IEEE Transactions on Multimedia
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Determining how a speaker is engaged in a conversation is crucial for achieving harmonious interaction between computers and humans. In this study, a fusion approach was developed based on psychological factors to recognize Interaction Style ($IS$ ) in spoken conversation, which plays a key role in creating natural dialogue agents. The proposed Fused Cross-Correlation Model (FCCM) provides a unified probabilistic framework to model the relationships among the psychological factors of emotion, personality trait ($PT$), transient $IS$, and $IS$ history, for recognizing $IS$. An emotional arousal-dependent speech recognizer was used to obtain the recognized spoken text for extracting linguistic features to estimate transient $IS$ likelihood and recognize $PT$. A temporal course modeling approach and an emotional sub-state language model, based on the temporal phases of an emotional expression, were employed to obtain a better emotion recognition result. The experimental results indicate that the proposed FCCM yields satisfactory results in $IS$ recognition and also demonstrate that combining psychological factors effectively improves $IS$ recognition accuracy.