Itegrating backchannel prediction models into embodied conversational agents

I.A. de Kok, Dirk K.J. Heylen

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    Abstract

    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 specication interface where the typical type of head nods to be displayed by the agent can be specied and ways to generate slight variations during runtime.
    Original languageEnglish
    Title of host publicationProceedings of the 12th International Conference on Intelligent Virtual Agents, IVA 2012
    Place of PublicationBerlin
    PublisherSpringer
    Pages268-274
    Number of pages7
    ISBN (Print)978-3-642-33196-1
    DOIs
    Publication statusPublished - Sep 2012
    Event12th International Conference on Intelligent Virtual Agents, IVA 2012 - Santa Cruz, United States
    Duration: 12 Sep 201214 Sep 2012
    Conference number: 12

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    Volume7502
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference12th International Conference on Intelligent Virtual Agents, IVA 2012
    Abbreviated titleIVA
    CountryUnited States
    CitySanta Cruz
    Period12/09/1214/09/12

    Keywords

    • HMI-IA: Intelligent Agents

    Cite this

    de Kok, I. A., & Heylen, D. K. J. (2012). Itegrating backchannel prediction models into embodied conversational agents. In Proceedings of the 12th International Conference on Intelligent Virtual Agents, IVA 2012 (pp. 268-274). (Lecture Notes in Computer Science; Vol. 7502). Berlin: Springer. https://doi.org/10.1007/978-3-642-33197-8_28