Abstract
Understanding how nonverbal aspects of communication support, complement, and in some cases, override verbal communication is necessary for human interactions. It is also crucial for designing and implementing interactive systems that aim at supporting flexible interaction management using natural language with users. In particular, the need for more comprehensive communication has become obvious in theubiquitous computing context where context-aware applications and automatic services require sophisticated knowledge management and adaptation to various user needs. Interactions with smart objects, services, and environments need to address challenges concerning natural, intuitive, easy, and friendly interaction. For instance, the Roadmap for Smart Human Environments (Plomp et al., 2002) envisages that smart environments will be populated by several context-aware devices which communicate with each other and with the users. The systems will identify their current context of use, adapt their behaviour accordingly, and also allow natural interaction.
In this chapter, I discuss verbal and nonverbal communication especially for the purposes of designing and developing interactive systems. I report on machinelearning experiments conducted on annotated gesture and facial expression data, and focus on the feedback and turn-taking processes that are important in building shared understanding of the semantics and flow of interaction.
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Jokinen, K. (2009). Nonverbal Feedback in Interactions. In: Tao, J., Tan, T. (eds) Affective Information Processing. Springer, London. https://doi.org/10.1007/978-1-84800-306-4_13
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DOI: https://doi.org/10.1007/978-1-84800-306-4_13
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