β Scribed by Sharon Oviatt, BjΓΆrn Schuller, Philip R. Cohen, Daniel Sonntag, Gerasimos Potamianos and Antonio KrΓΌger
No coin nor oath required. For personal study only.
Contents
Preface
Figure Credits
Introduction: Trends in Intelligent Multimodal-Multisensorial Interfaces: Cognition, Emotion, Social Signals, Deep Learning, and More
PART I. MULTIMODAL SIGNAL PROCESSING AND ARCHITECTURES
1. Challenges and Applications in Multimodal Machine Learning
2. Classifying Multimodal Data
3. Learning for Multimodal and Affect-Sensitive Interfaces
4. Deep Learning for Multisensorial and Multimodal Interaction
PART II. MULTIMODAL PROCESSING OF SOCIAL AND EMOTIONAL STATES
5. Multimodal User State and Trait Recognition: An Overview
6. Multimodal-Multisensor Affect Detection
7. Multimodal Analysis of Social Signals
8. Real-Time Sensing of Affect and Social Signals in a Multimodal Framework: A Practical Approach
9. How Do Users Perceive Multimodal Expressions of Affects?
PART III. MULTIMODAL PROCESSING OF COGNITIVE STATES
10. Multimodal Behavioral and Physiological Signals as Indicators of Cognitive Load
11. Multimodal Learning Analytics: Assessing Learnersβ Mental State During the Process of Learning
12. Multimodal Assessment of Depression from Behavioral Signals
13. Multimodal Deception Detection
PART IV. MULTIDISCIPLINARY CHALLENGE TOPIC
14. Perspectives on Predictive Power of Multimodal Deep Learning: Surprises and Future Directions
Index
Biographies
Volume 2 Glossary
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