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Earlier Identification of Children with Autism Spectrum Disorder: An Automatic Vocalisation-based Approach
Med Univ Graz, Dept Phoniatr, Res Unit iDN, Graz, Austria;Tech Univ Munich, Machine Intelligence & Signal Proc Grp MISP, MMK, Munich, Germany;BioTechMed Graz, BEE PRI, Graz, Austria.
Univ Passau, Chair Complex & Intelligent Syst, Passau, Germany;Imperial Coll London, Dept Comp, Machine Learning Grp, London, England.
Med Univ Graz, Dept Phoniatr, Res Unit iDN, Graz, Austria;BioTechMed Graz, BEE PRI, Graz, Austria;Karolinska Inst, Dept Womens & Childrens Hlth, Ctr Neurodev Disorders KIND, Stockholm, Sweden.
Tech Univ Munich, Machine Intelligence & Signal Proc Grp MISP, MMK, Munich, Germany;Nuance Commun, Ulm, Germany.
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2017 (English)In: 18Th Annual Conference Of The International Speech Communication Association (INTERSPEECH 2017), Vols 1-6: Situated Interaction, 2017, p. 309-313Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. p. 309-313
Series
Interspeech, ISSN 2308-457X
Keywords [en]
autism spectrum disorder, early identification, infant vocalisation analysis, speech-language pathology
National Category
Psychiatry
Identifiers
URN: urn:nbn:se:uu:diva-391311DOI: 10.21437/Interspeech.2017-1007ISI: 000457505000063ISBN: 978-1-5108-4876-4 (print)OAI: oai:DiVA.org:uu-391311DiVA, id: diva2:1344668
Conference
18th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2017), AUG 20-24, 2017, Stockholm, SWEDEN
Funder
EU, Horizon 2020, 688835Riksbankens JubileumsfondSwedish Research CouncilAvailable from: 2019-08-21 Created: 2019-08-21 Last updated: 2019-08-21Bibliographically approved

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Nyström, PärFalck-Ytter, Terje

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