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    Earlier identification of children with autism spectrum disorder: An automatic vocalisation-based approach

    Access Status
    Fulltext not available
    Authors
    Pokorny, F.
    Schuller, B.
    Marschik, P.
    Brueckner, R.
    Nyström, P.
    Cummins, N.
    Bolte, Sven
    Einspieler, C.
    Falck-Ytter, T.
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Pokorny, F. and Schuller, B. and Marschik, P. and Brueckner, R. and Nyström, P. and Cummins, N. and Bolte, S. et al. 2017. Earlier identification of children with autism spectrum disorder: An automatic vocalisation-based approach, pp. 309-313.
    Source Title
    Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
    DOI
    10.21437/Interspeech.2017-1007
    ISSN
    2308-457X
    School
    School of Occ Therapy, Social Work and Speech Path
    URI
    http://hdl.handle.net/20.500.11937/62419
    Collection
    • Curtin Research Publications
    Abstract

    Copyright © 2017 ISCA. Autism spectrum disorder (ASD) is a neurodevelopmental disorder usually diagnosed in or beyond toddlerhood. ASD is defined by repetitive and restricted behaviours, and deficits in social communication. The early speech-language development of individuals with ASD has been characterised as delayed. However, little is known about ASD-related characteristics of pre-linguistic vocalisations at the feature level. In this study, we examined pre-linguistic vocalisations of 10-month-old individuals later diagnosed with ASD and a matched control group of typically developing individuals (N = 20). We segmented 684 vocalisations from parent-child interaction recordings. All vocalisations were annotated and signal-analytically decomposed. We analysed ASD-related vocalisation specificities on the basis of a standardised set (eGeMAPS) of 88 acoustic features selected for clinical speech analysis applications. 54 features showed evidence for a differentiation between vocalisations of individuals later diagnosed with ASD and controls. In addition, we evaluated the feasibility of automated, vocalisation-based identification of individuals later diagnosed with ASD.We compared linear kernel support vector machines and a 1-layer bidirectional long short-term memory neural network. Both classification approaches achieved an accuracy of 75% for subject-wise identification in a subject-independent 3-fold cross-validation scheme. Our promising results may be an important contribution en-route to facilitate earlier identification of ASD.

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