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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Combining voice and language features improves automated autism detection.

Heather MacFarlane¹, Alexandra C Salem¹, Liu Chen^2,3

¹Department of Psychiatry, Oregon Health & Science University, Portland, Oregon, USA.

Autism Research : Official Journal of the International Society for Autism Research

|April 23, 2022

Summary

Automated analysis of language and voice in children with autism spectrum disorder (ASD) shows promise. Objective measurements accurately predict diagnosis, offering a potential tool for future research outcome measures.

Keywords:

autism automated measures communication disfluency natural language processing pragmatic language prosody voice

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Published on: December 14, 2012

Area of Science:

Computational linguistics
Speech analysis
Autism spectrum disorder (ASD) research

Background:

Autism spectrum disorder (ASD) is characterized by communication challenges, including language and prosody variations.
Current clinical assessments for ASD are often time-consuming and expensive.
Objective and scalable methods are needed to quantify language and voice characteristics in ASD.

Purpose of the Study:

To develop and validate automated methods for classifying individuals with and without ASD using language and voice data.
To assess the efficacy of natural language processing (NLP) and harmonic speech models in ASD detection.
To explore the potential of these automated measures as outcome indicators in ASD research.

Main Methods:

Analysis of language transcripts and audio recordings from 158 children (88 ASD, 70 non-ASD) aged 7-17 using the Autism Diagnostic Observation Schedule (ADOS-2).
Generation of seven automated language measures (ALMs) and ten automated voice measures (AVMs) from ADOS-2 tasks.
Classification using support vector machine (SVM) and receiver operating characteristic (ROC) analysis to determine model performance.

Main Results:

The automated voice measure (AVM) model achieved an Area Under the Curve (AUC) of 0.7800.
The automated language measure (ALM) model achieved a higher AUC of 0.8748, particularly effective for younger children with lower language skills.
The combined ALM and AVM model yielded a significantly improved AUC of 0.9205, demonstrating robust classification accuracy.

Conclusions:

Automated analysis of language and voice characteristics can effectively differentiate children with and without ASD.
Combining language and voice measures offers a powerful, objective approach for ASD identification.
This methodology presents a viable strategy for developing scalable and cost-effective outcome measures in ASD research.