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GNetIc – Using Bayesian Decision Networks for Iconic Gesture Generation
Expressing spatial information with iconic gestures is abundant in human communication and requires to transform a referent representation into resembling gestural form. This task is challenging as the mapping is determined by the visuo-spatial features of the referent, the overall discourse context as well as concomitant speech, and its outcome varies considerably across different speakers. We present a framework, GNetIc, that combines data-driven with model-based techniques to model the generation of iconic gestures with Bayesian decision networks. Drawing on extensive empirical data, we discuss how this method allows for simulating speaker-specific vs. speaker-independent gesture production. Modeling results from a prototype implementation are presented and evaluated.
| Download Paper | |
| Full Title | GNetIc – Using Bayesian Decision Networks for Iconic Gesture Generation |
| Year | 2009 |
| Language | English |
| Book | Intelligent Virtual Agents - IVA 2009 |
| Keywords | Nonverbal Behavior, Gesture Generation, Inter-subjective Differences, Bayesian Decision Networks |
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