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What is Multimodal Emotion Recognition

Handbook of Research on Instructional Systems and Technology
Multimodal emotion recognition is where one is inferring an emotional state and integrating inputs coming from multiple emotion-sensitive sources.
Published in Chapter:
Addressing Emotions within E-Learning Systems
Valentino Zurloni (CESCOM, University of Milan - Bicocca, Italy), Fabrizia Mantovani (CESCOM, University of Milan - Bicocca, Italy, and ATN-P LAB, Istituto Auxologico Italiano, Italy), Marcello Mortillaro (CESCOM, University of Milan - Bicocca, Italy, and CISA - University of Geneva, Switzerland), Antonietta Vescovo (CESCOM, University of Milan - Bicocca, Italy), and Luigi Anolli (CESCOM, University of Milan - Bicocca, Italy)
Copyright: © 2008 |Pages: 14
DOI: 10.4018/978-1-59904-865-9.ch057
Abstract
Emotions are attracting growing attention within the instructional design research community. However, clarification is still required as to how exactly to address emotions within the field of e-learning. The aim of this chapter is twofold. Firstly, we will focus on the reasons for including emotions within the instructional technology domain, and in particular, on the relevance of emotions to computer-based learning. The need for specific theory in this regard is heightened by the current drive to design instructional devices that interact with learners in a motivating, engaging, and helpful way. Secondly, within the framework affective computing paradigm, the different modalities for detecting emotions in instructional technology contexts will be systematically reviewed, and the strengths and limits of each will be discussed on the basis of the most up to date research outcomes. Finally, a tentative architecture for emotion recognition in computer-based learning will be proposed, focusing on the adoption of a multimodal approach to emotion recognition, in order to overcome the limitations and the difficulties associated with individual modalities.
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More Results
Machine Learning and Emotions: The Hidden Language in Your Voice
The use of many sources of information, such as facial expressions, speech, body language, and physiological signals, to improve the accuracy and reliability of emotion detection systems by taking into account a wider range of clues.
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