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Autism Spectrum Disorder

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Related Experiment Video

Updated: Jan 16, 2026

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Published on: October 3, 2018

Screening autism spectrum disorder in children using machine learning on speech transcripts.

Rida Assaf¹, Zein Shehabeddine², Vikram Ramesh³

¹Department of Computer Science, American University of Beirut, Beirut, Lebanon. ra278@aub.edu.lb.

Scientific Reports

|October 1, 2025

Summary

This study shows that machine learning models using children's speech transcripts can detect Autism Spectrum Disorder (ASD) with over 86% accuracy. This privacy-preserving method offers a faster, more ethical alternative to traditional diagnostic tools.

Keywords:

Artificial intelligence Autism spectrum disorder (ASD)Computational linguistics Feature importance Machine learning Privacy

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Area of Science:

Computational Linguistics
Developmental Psychology
Machine Learning

Background:

Early detection of Autism Spectrum Disorder (ASD) is vital for effective intervention and improved developmental outcomes.
Traditional ASD diagnostic methods are often time-consuming, resource-intensive, and raise privacy concerns, especially for minors.
There is a need for non-invasive and ethical approaches to ASD detection.

Purpose of the Study:

To evaluate the feasibility of privacy-preserving machine learning models for ASD detection using children's speech transcripts.
To assess the effectiveness of text-based linguistic features for identifying ASD.
To explore methods that minimize privacy risks associated with sensitive biometric data.

Main Methods:

Utilized machine learning models trained on structured text-based speech data from children.
Focused on linguistic features such as Mean Length of Utterance (MLU) and Mean Length of Turn Ratio (MLT Ratio).
Conducted experiments on two datasets from the TalkBank repository, prioritizing privacy by avoiding raw audio/video data.

Main Results:

Machine learning models achieved predictive accuracy exceeding 86% on both datasets.
A small subset of linguistic features proved sufficient for high performance, reducing data collection needs.
The text-based approach inherently minimized privacy risks by excluding identifiable biometric data.

Conclusions:

Privacy-preserving machine learning models using speech transcripts show significant promise for early ASD detection.
Computational linguistics offers a non-invasive and ethical foundation for future ASD diagnostic tools in clinical and educational settings.
This approach enhances privacy by focusing on structured linguistic data rather than sensitive biometric information.