Multimodal Social Signal Processing and Application

Social Signal is observed as the joint information revealed from the signals of multimodal channels such as language, voice, gaze, posture, gestures, and biological information. Social Signal Processing is conducted in constructing computational models to sense and understand human social signals including emotion, attitude, personality, skill, role, and other forms of communication between humans. It is a technology for understanding and modeling the social aspects of human beings through their communicational activities. Social signal processing is available to develop new technologies for human-human and human-computer interactions (i.e. the interaction between humans and computers, virtual agents, robots and other artifacts). In recent years, much attention has been gathered on such multimodal social signal processing technologies and their applications.

To further improve these studies, in addition to the fields in the computer science discipline such as AI, NLP, signal processing, ML, and HCI, other disciplines like linguistics, psychology, and sociology play important roles in providing the theoretical backgrounds of human communication. In other words, this is an exciting research area that has the potential to encourage collaborations between researchers in a wide variety of disciplines and to conduct new interdisciplinary ideas.

We welcome submissions from all research fields related to multimodal social signal processing and its applications. For example, topics like theoretical foundations, empirical verifications, analysis as well as component technologies, integrations, interface designs, and system developments. Submissions from behavioral science and other social sciences are also welcomed. They are expected to broaden computer scientists’ view and prevent potential over-focused motivation in pursuing the novelty of techniques and algorithms.