Print Email Facebook Twitter An expressive virtual audience with flexible behavioral styles Title An expressive virtual audience with flexible behavioral styles Author Kang, N. Brinkman, W.P. van Riemsdijk, M.B. Neerincx, M.A. Publication year 2013 Abstract Currently, expressive virtual humans are used in psychological research, training, and psychotherapy. However, the behavior of these virtual humans is usually scripted and therefore cannot be modified freely at runtime. To address this, we created a virtual audience with parameterized behavioral styles. This paper presents a parameterized audience model based on probabilistic models abstracted from the observation of real human audiences (n = 16). The audience's behavioral style is controlled by model parameters that define virtual humans' moods, attitudes, and personalities. Employing these parameters as predictors, the audience model significantly predicts audience behavior. To investigate if people can recognize the designed behavioral styles generated by this model, 12 audience styles were evaluated by two groups of participants. One group (n = 22) was asked to describe the virtual audience freely, and the other group (n = 22) was asked to rate the audiences on eight dimensions. The results indicated that people could recognize different audience attitudes and even perceive the different degrees of certain audience attitudes. In conclusion, the audience model can generate expressive behavior to show different attitudes by modulating model parameters. © 2010-2012 IEEE. Subject HumanPCS - Perceptual and Cognitive SystemsBSS - Behavioural and Societal SciencesVirtual environments and GamingInformation SocietyExpressive listening behaviorParameterized audience modelPublic speakingVirtual agents To reference this document use: http://resolver.tudelft.nl/uuid:bf790abe-acd6-48fc-8d8b-ee214196f287 DOI https://doi.org/10.1109/taffc.2013.2297104 TNO identifier 493091 Publisher Institute of Electrical and Electronics Engineers Inc. ISSN 1949-3045 Source IEEE Transactions on Affective Computing, 4 (4), 326-340 Article number 6714373 Document type article Files To receive the publication files, please send an e-mail request to TNO Library.