Manipulating the Perception of Virtual Audiences using Crowdsourced Behaviors (bibtex)
by Mathieu Chollet, Nithin Chandrashekhar, Ari Shapiro, Louis-Philippe Morency, Stefan Scherer
Abstract:
Virtual audiences are used for training public speaking and mitigating anxiety related to it. However, research has been scarce on studying how virtual audiences are perceived and which non-verbal behaviors should be used to make such an audience appear in particular states, such as boredom or engagement. Recently, crowdsourcing methods have been proposed for collecting data for building virtual agents' behavior models. In this paper, we use crowdsourcing for creating and evaluating a nonverbal behaviors generation model for virtual audiences. We show that our model successfully expresses relevant audience states (i.e. low to high arousal, negative to positive valence), and that the overall impression exhibited by the virtual audience can be controlled my manipulating the amount of individual audience members that display a congruent state.
Reference:
Manipulating the Perception of Virtual Audiences using Crowdsourced Behaviors (Mathieu Chollet, Nithin Chandrashekhar, Ari Shapiro, Louis-Philippe Morency, Stefan Scherer), In Proceedings of the IVA 2016 : Intelligent Virtual Agents Conference, Springer, 2016.
Bibtex Entry:
@inproceedings{chollet_manipulating_2016,
	address = {Los Angeles, CA},
	title = {Manipulating the {Perception} of {Virtual} {Audiences} using {Crowdsourced} {Behaviors}},
	url = {http://iva2016.ict.usc.edu/wp-content/uploads/Papers/100110162.pdf},
	abstract = {Virtual audiences are used for training public speaking and mitigating anxiety related to it. However, research has been scarce on studying how virtual audiences are perceived and which non-verbal behaviors should be used to make such an audience appear in particular states, such as boredom or engagement. Recently, crowdsourcing methods have been proposed for collecting data for building virtual agents' behavior models. In this paper, we use crowdsourcing for creating and evaluating a nonverbal behaviors generation model for virtual audiences. We show that our model successfully expresses relevant audience states (i.e. low to high arousal, negative to positive valence), and that the overall impression exhibited by the virtual audience can be controlled my manipulating the amount of individual audience members that display a congruent state.},
	booktitle = {Proceedings of the {IVA} 2016 : {Intelligent} {Virtual} {Agents} {Conference}},
	publisher = {Springer},
	author = {Chollet, Mathieu and Chandrashekhar, Nithin and Shapiro, Ari and Morency, Louis-Philippe and Scherer, Stefan},
	month = sep,
	year = {2016},
	keywords = {UARC, Virtual Humans}
}
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