Affective Computing

Scope

Emotion is known as the grammar of social living. Affective computing aims to create computing systems that can perceive, recognize, and understand human emotions, and respond emotionally, sensitively, and naturally to them. It seeks to build a strong foundation for natural anthropomorphic human-computer interaction. It is an important part of intelligent computing, and it also provides new ideas for artificial intelligence decision-making. It is of great value in opening the era of intelligence and digitalization. Current trends in the field of affective computing include fine-grained emotion classification, multi-model reasoning, and empathetic interaction, which all require innovative computing models and intelligent algorithms.

This special issue focuses on the subject of Affective Computing and aims to bring together and disseminate the latest advances in the design and optimization of affective computing systems using modern deep and general machine learning tools and techniques.

Topics of Interest
This special issue solicits original research as well as review articles. Topics of interest include, but are not limited to:

• Emotion recognition and generation methods based on/for spoken and written text.
• Emotion recognition and generation methods based on/for audio and speech signals.
• Emotion recognition and generation methods based on/for visual signals such as facial expressions and body postures.
• Emotion recognition methods based on fMRI, EEG, and other physiological signals.
• Multimodal emotion recognition and generation methods.
• Empathic response generation in multiple rounds of (potentially multimodal) dialogue.
• Construction of affective computing datasets and related data collection methods.
• Affective computing and dialogue generation systems (e.g., ChatGPT, GPT-4, etc.).
• Trustworthy affective computing methods, including dependable, ethical, explainable, fair, green, personalized, and safe processing.
• Novel approaches towards efficient learning from big and little data in affective computing.
• Usage of foundation models such as general pretrained transformers in affective computing.
• Applications and evaluation of affective computing in the real world, such as education and training, healthcare, business services, industrial design, social governance, etc.
• Multi-model causal reasoning of emotion.

Guest Editors

Björn Schuller
Imperial College London, United Kingdom

Bao-Liang Lu
Shanghai Jiao Tong University, China

Guoying Zhao
University of Oulu, Finland

Taihao Li
Zhejiang Lab, China

Zhao Lv
Anhui University, China

Submission Instructions

Please indicate in your cover letter that your submission is intended for inclusion in the special issue.

Submission Deadline: July 20, 2023

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