In this paper we will present our design for generating listening behavior for embodied conversational agents. It uses a corpus based prediction model to predict the timing of backchannels. The design of the system iterates on a previous design (Huang et al. [5]) on which we propose improvements in terms of robustness and personalization. For robustness we propose a variable threshold determined at run-time to regulate the amount of backchannels being produced by the system. For personalization we propose a character specification interface where the typical type of head nods to be displayed by the agent can be specified and ways to generate slight variations during runtime.
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