Vocal communication of emotion: a review of research paradigms
Speech Communication - Special issue on speech and emotion
User Modeling and User-Adapted Interaction
E-Drama: Facilitating Online Role-play using an AI Actor and Emotionally Expressive Characters
International Journal of Artificial Intelligence in Education
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Recognition of affect, judgment, and appreciation in text
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Planning Small Talk behavior with cultural influences for multiagent systems
Computer Speech and Language
Exploitation of Contextual Affect-Sensing and Dynamic Relationship Interpretation
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
The Semantic Vectors Package: New Algorithms and Public Tools for Distributional Semantics
ICSC '10 Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing
Smile When You Read This, Whether You Like It or Not: Conceptual Challenges to Affect Detection
IEEE Transactions on Affective Computing
Affect and metaphor sensing in virtual drama
International Journal of Computer Games Technology
A multimodal database for mimicry analysis
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
| Hi-index | 0.00 |
Affect detection from open-ended virtual improvisational contexts is a challenging task. To achieve this research goal, the authors developed an intelligent agent which was able to engage in virtual improvisation and perform sentence-level affect detection from user inputs. This affect detection development was efficient for the improvisational inputs with strong emotional indicators. However, it can also be fooled by the diversity of emotional expressions such as expressions with weak or no affect indicators or metaphorical affective inputs. Moreover, since the improvisation often involves multi-party conversations with several threads of discussions happening simultaneously, the previous development was unable to identify the different discussion contexts and the most intended audiences to inform affect detection. Therefore, in this paper, the authors employ latent semantic analysis to find the underlying semantic structures of the emotional expressions and identify topic themes and target audiences especially for those inputs without strong affect indicators to improve affect detection performance. They also discuss how such semantic interpretation of dialog contexts is used to identify metaphorical phenomena. Initial exploration on affect detection from gestures is also discussed to interpret users' experience of using the system and provide an extra channel to detect affect embedded in the virtual improvisation. Their work contributes to the journal themes on affect sensing from text, semantic-based dialogue processing and emotional gesture recognition.