The SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent

G. McKeown, M.F. Valstar, R. Cowie, Maja Pantic, M. Schroeder

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    543 Citations (Scopus)
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    Abstract

    SEMAINE has created a large audiovisual database as a part of an iterative approach to building Sensitive Artificial Listener (SAL) agents that can engage a person in a sustained, emotionally colored conversation. Data used to build the agents came from interactions between users and an "operator‿ simulating a SAL agent, in different configurations: Solid SAL (designed so that operators displayed an appropriate nonverbal behavior) and Semi-automatic SAL (designed so that users' experience approximated interacting with a machine). We then recorded user interactions with the developed system, Automatic SAL, comparing the most communicatively competent version to versions with reduced nonverbal skills. High quality recording was provided by five high-resolution, high-framerate cameras, and four microphones, recorded synchronously. Recordings total 150 participants, for a total of 959 conversations with individual SAL characters, lasting approximately 5 minutes each. Solid SAL recordings are transcribed and extensively annotated: 6-8 raters per clip traced five affective dimensions and 27 associated categories. Other scenarios are labeled on the same pattern, but less fully. Additional information includes FACS annotation on selected extracts, identification of laughs, nods, and shakes, and measures of user engagement with the automatic system. The material is available through a web-accessible database.
    Original languageUndefined
    Pages (from-to)5-17
    Number of pages13
    JournalIEEE transactions on affective computing
    Volume3
    Issue number1
    DOIs
    Publication statusPublished - Jan 2012

    Keywords

    • Social Signal Processing
    • EWI-22960
    • HMI-MI: MULTIMODAL INTERACTIONS
    • IR-84220
    • Emotional Corpora
    • affective annotation
    • METIS-296252
    • Affective Computing

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