Detecting Abnormal Word Utterances in Children With Autism Spectrum Disorders

Abnormal prosody is often evident in the voice intonations of individuals with autism spectrum disorders. We compared a machine-learning-based voice analysis with human hearing judgments made by 10 speech therapists for classifying children with autism spectrum disorders (n = 30) and typical develop...

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Bibliographic Details
Published inPerceptual and motor skills Vol. 124; no. 5; pp. 961 - 973
Main Authors Nakai Yasushi, Takiguchi Tetsuya, Matsui Gakuyo, Yamaoka Noriko, Takada Satoshi
Format Journal Article
LanguageEnglish
Published Missoula SAGE PUBLICATIONS, INC 01.10.2017
Subjects
Autism
Autistic children
Children
Intonation
Machine learning
Prosody
Speech therapists
Speech-language pathologists
Therapy
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Summary:Abnormal prosody is often evident in the voice intonations of individuals with autism spectrum disorders. We compared a machine-learning-based voice analysis with human hearing judgments made by 10 speech therapists for classifying children with autism spectrum disorders (n = 30) and typical development (n = 51). Using stimuli limited to single-word utterances, machine-learning-based voice analysis was superior to speech therapist judgments. There was a significantly higher true-positive than false-negative rate for machine-learning-based voice analysis but not for speech therapists. Results are discussed in terms of some artificiality of clinician judgments based on single-word utterances, and the objectivity machine-learning-based voice analysis adds to judging abnormal prosody.
ISSN:0031-5125
1558-688X
DOI:10.1177/0031512517716855