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    Multimodal models for contextual affect assessment in real-time

    Access Status
    Fulltext not available
    Authors
    Vice, J.
    Khan, Masood
    Yanushkevich, S.
    Date
    2019
    Type
    Conference Paper
    
    Metadata
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    Citation
    Vice, J. and Khan, M.M. and Yanushkevich, S. 2019. Multimodal models for contextual affect assessment in real-time, in Proceedings of the 2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI), Dec 12-14 2019. Los Angeles, USA: IEEE.
    Source Title
    Proceedings - 2019 IEEE 1st International Conference on Cognitive Machine Intelligence, CogMI 2019
    DOI
    10.1109/CogMI48466.2019.00020
    ISBN
    9781728167374
    Faculty
    Faculty of Science and Engineering
    School
    School of Civil and Mechanical Engineering
    URI
    http://hdl.handle.net/20.500.11937/80264
    Collection
    • Curtin Research Publications
    Abstract

    Most affect classification schemes rely on near accurate single-cue models resulting in less than required accuracy under certain peculiar conditions. We investigate how the holism of a multimodal solution could be exploited for affect classification. This paper presents the design and implementation of a prototype, stand-alone, real-time multimodal affective state classification system. The presented system utilizes speech and facial muscle movements to create a holistic classifier. The system combines a facial expression classifier and a speech classifier that analyses speech through paralanguage and propositional content. The proposed classification scheme includes a Support Vector Machine (SVM) - paralanguage; a K-Nearest Neighbor (KNN) - propositional content and an InceptionV3 neural network - facial expressions of affective states. The SVM and Inception models boasted respective validation accuracies of 99.2% and 92.78%.

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