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Acoustic analysis in stuttering: a machine-learning study.

Francesco Asci^1,2, Luca Marsili³, Antonio Suppa^1,2

¹Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy.

Frontiers in Neurology

|July 17, 2023

Summary

This summary is machine-generated.

Artificial intelligence accurately identifies stuttering using voice analysis, aiding objective diagnosis. This machine learning approach is reliable across speech tasks and can be used remotely via smartphones.

Keywords:

acoustic analysis home environment machine-learning stuttering telemedicine

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Area of Science:

Speech-language pathology
Biomedical engineering
Computational linguistics

Background:

Stuttering is a neurodevelopmental disorder impacting speech fluency, typically diagnosed via subjective perceptual evaluation.
Current diagnostic methods rely on clinical scales and perceptual assessments, lacking objective measures.
Acoustic analysis offers a promising avenue for the objective assessment of dysfluency in people who stutter (PWS).

Purpose of the Study:

To objectively and automatically assess stuttering using artificial intelligence (AI), specifically a support vector machine (SVM) classifier.
To investigate age-related voice changes in individuals who stutter (PWS).
To determine the effectiveness of specific speech tasks for objective and automatic stuttering assessment.

Main Methods:

Recruited 53 PWS (20 children, 33 adults) and 71 age/gender-matched controls.
Recorded voluntary vowel emissions and sentences via smartphones.
Analyzed audio samples using an SVM machine learning algorithm and artificial neural networks (ANN) for correlations.

Main Results:

Machine learning accurately discriminated between PWS and controls (88% accuracy).
Physiologic aging significantly influenced stuttering, with high accuracy (92%) in classifying children and adults.
Diagnostic accuracy was consistent across different speech tasks, with strong clinical-instrumental correlations.

Conclusions:

AI-powered acoustic analysis (SVM) provides a reliable method for objective, automatic stuttering recognition.
The high accuracy is independent of the speech task, aiding clinical diagnosis and management.
Smartphone-based audio collection enables potential telemedicine applications for stuttering assessment in home environments.