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Autism Spectrum Disorder

Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
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Related Experiment Video

Updated: Oct 19, 2025

Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers

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Quantifying Voice Characteristics for Detecting Autism.

Meysam Asgari¹, Liu Chen¹, Eric Fombonne²

¹Institute on Development and Disability, Department of Pediatrics, Oregon Health & Science University, Portland, OR, United States.

Frontiers in Psychology

|September 24, 2021

Summary

This summary is machine-generated.

This study introduces an automated method to analyze speech prosody in autism spectrum disorder (ASD). This approach quantifies subtle vocal differences, aiding in early detection and treatment research for autism.

Keywords:

autism spectrum disorder harmonic model machine learning prosody speech analysis voice

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Area of Science:

Neuroscience
Computational Linguistics
Speech Science

Background:

Clinicians recognize prosodic anomalies in autism but lack quantitative tools.
Manual analysis of speech patterns in autism is time-consuming and subjective.

Purpose of the Study:

To develop an automated system for quantifying prosodic abnormalities in individuals with autism.
To create fine-grained, objective speech measures for autism detection and research.

Main Methods:

Utilized a harmonic model (HM) to analyze the harmonic content of speech signals.
Computed quantitative measures from harmonic content and standard speech features (e.g., loudness).
Trained machine learning models to differentiate between individuals with autism and typical development (TD).

Main Results:

Machine learning models successfully distinguished individuals with autism from TD controls.
The automated approach demonstrated significantly better performance than chance.
The study involved 118 youth (90 with autism, 28 TD), with a mean age of 10.9 years.

Conclusions:

Automated speech and voice analysis offers a viable method for autism detection.
These quantitative measures can serve as novel outcomes for autism treatment research.
Potential for early autism detection in at-risk infants and toddlers.