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Percept Mot Skills. 2017 Oct;124(5):961-973. doi: 10.1177/0031512517716855. Epub 2017 Jun 26.

Detecting Abnormal Word Utterances in Children With Autism Spectrum Disorders: Machine-Learning-Based Voice Analysis Versus Speech Therapists.

Author information

1
1 University of Miyazaki, Miyazaki, Japan.
2
2 Kobe University, Kobe, Japan.
3
3 Osaka International College, Osaka, Japan.
4
4 Kobe Tokiwa University, Kobe, Japan.

Abstract

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 ( nā€‰=ā€‰30) and typical development ( nā€‰=ā€‰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.

KEYWORDS:

F-measure; abnormal prosody; autism spectrum disorder; machine-learning-based voice analysis; speech therapy

PMID:
28649923
DOI:
10.1177/0031512517716855
[Indexed for MEDLINE]

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