2005 Special Issue: Challenges in real-life emotion annotation and machine learning based detection
Neural Networks - Special issue: Emotion and brain
Emotion recognition from text using semantic labels and separable mixture models
ACM Transactions on Asian Language Information Processing (TALIP)
Predicting student emotions in computer-human tutoring dialogues
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Real-Life Emotion Recognition in Speech
Speaker Classification II
A three-layered model for expressive speech perception
Speech Communication
ITSPOKE: an intelligent tutoring spoken dialogue system
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Manual annotation of opinion categories in meetings
LAC '06 Proceedings of the Workshop on Frontiers in Linguistically Annotated Corpora 2006
Multi-stage classification of emotional speech motivated by a dimensional emotion model
Multimedia Tools and Applications
Human emotion recognition from videos using spatio-temporal and audio features
The Visual Computer: International Journal of Computer Graphics
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Detecting emotions in the context of automated call center services can be helpful for following the evolution of the human-computer dialogues, enabling dynamic modification of the dialogue strategies and influencing the final outcome. The emotion detection work reported here is a part of larger study aiming to model user behavior in real interactions. We make use of a corpus of real agent-client spoken dialogues in which the manifestation of emotion is quite complex, and it is common to have shaded emotions since the interlocutors attempt to control the expression of their internal attitude. Our aims are to define appropriate emotions for call center services, to annotate the dialogues and to validate the presence of emotions via perceptual tests and to find robust cues for emotion detection. In contrast to research carried out with artificial data with simulated emotions, for real-life corpora the set of appropriate emotion labels must be determined. Two studies are reported: the first investigates automatic emotion detection using linguistic information, whereas the second concerns perceptual tests for identifying emotions as well as the prosodic and textual cues which signal them. About 11% of the utterances are annotated with non-neutral emotion labels. Preliminary experiments using lexical cues detect about 70% of these labels.