Prosodic characteristics in young children with autism spectrum disorder

Dilley, Laura C.; Cook, Sara; Stockman, Ida J.; Ingersoll, Brooke

doi:10.1121/1.4900376

Cited by 2 publications

(1 citation statement)

References 35 publications

(40 reference statements)

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“…Early speech-language development of children with ASD has repeatedly been characterised as delayed and deviant: Late onset canonical babbling, reduced volubility, and/or monotony in intonation are among the reported signs (e. g., [7,8,9,10]). Only a few studies focussed on early ASD-related vocalisation specificities at the feature level (e. g., [11]).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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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