Multimodal End-of-Turn Prediction in Multi-Party Meetings

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

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    44 Citations (Scopus)
    56 Downloads (Pure)


    One of many skills required to engage properly in a conversation is to know the appropiate use of the rules of engagement. In order to engage properly in a conversation, a virtual human or robot should, for instance, be able to know when it is being addressed or when the speaker is about to hand over the turn. The paper presents a multimodal approach to end-of-speaker-turn prediction using sequential probabilistic models (Conditional Random Fields) to learn a model from observations of real-life multi-party meetings. Although the results are not as good as expected, we provide insight into which modalities are important when taking a multimodal approach to the problem based on literature and our own results.
    Original languageUndefined
    Title of host publicationProceedings of the International Conference on Multimodal Interfaces, ICMI-MLMI 2009
    Place of PublicationNew York
    PublisherAssociation for Computing Machinery
    Number of pages8
    ISBN (Print)978-1-60558-772-1
    Publication statusPublished - 2009
    Event11th International Conference on Multimodal Interfaces, ICMI 2009 - Boston, United States
    Duration: 2 Nov 20094 Nov 2009
    Conference number: 11

    Publication series



    Conference11th International Conference on Multimodal Interfaces, ICMI 2009
    Abbreviated titleICMI
    Country/TerritoryUnited States


    • METIS-268953
    • Machine Learning
    • IR-73121
    • End-of-Turn Prediction
    • EWI-17022
    • EC Grant Agreement nr.: FP7/211486
    • HMI-IA: Intelligent Agents
    • HMI-HF: Human Factors
    • Multimodal

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