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  • Pokorny, Florian B.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 (författare)

Earlier Identification of Children with Autism Spectrum Disorder : An Automatic Vocalisation-based Approach

  • Artikel/kapitelEngelska2017

Förlag, utgivningsår, omfång ...

  • 2017
  • printrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:uu-391311
  • https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-391311URI
  • https://doi.org/10.21437/Interspeech.2017-1007DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:kon swepub-publicationtype

Anmärkningar

  • 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.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Schuller, Bjoern W.Univ Passau, Chair Complex & Intelligent Syst, Passau, Germany;Imperial Coll London, Dept Comp, Machine Learning Grp, London, England (författare)
  • Marschik, Peter B.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 (författare)
  • Brueckner, RaymondTech Univ Munich, Machine Intelligence & Signal Proc Grp MISP, MMK, Munich, Germany;Nuance Commun, Ulm, Germany (författare)
  • Nyström, Pär,1975-Uppsala universitet,Institutionen för psykologi(Swepub:uu)panys031 (författare)
  • Cummins, NicholasUniv Passau, Chair Complex & Intelligent Syst, Passau, Germany (författare)
  • Bolte, SvenKarolinska Inst, Dept Womens & Childrens Hlth, Ctr Neurodev Disorders KIND, Stockholm, Sweden;Stockholm Cty Council, Ctr Psychiat Res, Child & Adolescent Psychiat, Stockholm, Sweden (författare)
  • Einspieler, ChristaMed Univ Graz, Dept Phoniatr, Res Unit iDN, Graz, Austria (författare)
  • Falck-Ytter, TerjeUppsala universitet,Institutionen för psykologi,Karolinska Inst, Dept Womens & Childrens Hlth, Ctr Neurodev Disorders KIND, Stockholm, Sweden(Swepub:uu)terfa327 (författare)
  • 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, AustriaUniv Passau, Chair Complex & Intelligent Syst, Passau, Germany;Imperial Coll London, Dept Comp, Machine Learning Grp, London, England (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:18Th Annual Conference Of The International Speech Communication Association (INTERSPEECH 2017), Vols 1-6, s. 309-3139781510848764

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