#3687. Analysis and classification of speech sounds of children with autism spectrum disorder using acoustic features

October 2026publication date
Proposal available till 03-06-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Language and Linguistics;
Linguistics and Language;
Sociology and Political Science;
Speech and Hearing;
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Abstract:
Children with autism spectrum disorder (ASD) produce speech sounds different from that of Normal or non-ASD children. Acoustic features are analyzed first and then classification of ASD vs Normal speech is attempted using different machine learning techniques. Changes in the speech production characteristics of children are explored using three sets of features. Various combinations of the acoustic features are classified utilizing machine learning methods such as probabilistic neural network (PNN), multilayer perceptron (MLP), support vector machine (SVM), and K-nearest neighbors (KNN). The observations and this study results may be useful as acoustic biomarkers to identify autism and its progression/cure among children.
Keywords:
Autism spectrum disorder; Dominant frequencies; English vowels; Formant frequencies; p-value; Probabilistic neural network; Strength of excitation

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