'You are Sooo Cool, Valentina!' Recognizing Social Attitude in Speech-Based Dialogues with an ECA

Authors:
Fiorella Rosis;Anton Batliner;Nicole Novielli;Stefan Steidl
Affiliations:
Intelligent Interfaces, Department of Informatics, University of Bari, Via Orabona 4, 70126 Bari, Italy;Lehrstuhl für Mustererkennung, Universität Erlangen-Nürnberg, Martensstrasse 3, 91058 Erlangen, F.R. of Germany;Intelligent Interfaces, Department of Informatics, University of Bari, Via Orabona 4, 70126 Bari, Italy;Lehrstuhl für Mustererkennung, Universität Erlangen-Nürnberg, Martensstrasse 3, 91058 Erlangen, F.R. of Germany
Venue:
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Year:
2007

Citing 5
Cited 2

Social influence within immersive virtual environments

The social life of avatars
How to find trouble in communication

Speech Communication - Special issue on speech and emotion
Towards emotion prediction in spoken tutoring dialogues

NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
The independent and interactive effects of embodied-agent appearance and behavior on self-report, cognitive, and behavioral markers of copresence in immersive virtual environments

Presence: Teleoperators and Virtual Environments
User modeling and adaptation in health promotion dialogs with an animated character

Journal of Biomedical Informatics - Special issue: Dialog systems for health communications

Whodunnit - Searching for the most important feature types signalling emotion-related user states in speech

Computer Speech and Language
Segmenting into adequate units for automatic recognition of emotion-related episodes: a speech-based approach

Advances in Human-Computer Interaction - Special issue on emotion-aware natural interaction

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a method to recognize the 'social attitude' of users towards an Embodied Conversational Agent (ECA) from a combination of linguistic and prosodic features. After describing the method and the results of applying it to a corpus of dialogues collected with a Wizard of Oz study, we discuss the advantages and disadvantages of statistical and machine learning methods if compared with other knowledge-based methods.