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Cited by 22Autism Spectrum Disorder ResearchVoice and Speech DisordersSpeech Recognition and Synthesis

Classifying Speech of ASD Affected and Normal Children Using Acoustic Features

Mohanta, Abhijit, Mittal, Vinay Kumar·2020·2020 National Conference on Communications (NCC)
DOI 10.1109/ncc48643.2020.9056084
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Abstract

Children affected with autism spectrum disorder (ASD) produce speech that consists of distinctive acoustic patterns, as compared to normal children. Hence, acoustic analyses can help classifying speech of ASD affected children from that of normal children. In this study, the aim is to identify those discriminating characteristics of speech production that help classification between speech of children with ASD and normal children. Two separate datasets were recorded for this study: the English speech of children affected with ASD and the English speech of normal children. Comparative analyses of acoustic features derived for both datasets are carried out. Changes in the speech production characteristics are examined in three parts. Firstly, changes in the excitation source features F0 and strength of excitation (SoE) are analyzed. Secondly, changes in the vocal tract filter features the formants (F1 to F5) and dominant frequencies (FD1, FD2) are analyzed. Thirdly, changes in the combined source-filter features signal energy and zero-crossing rate are analyzed. Different combinations of the feature sets are then classified using three different classifiers for validation of results: SVM, KNN and ensemble classifiers. Performance evaluation is carried using different combinations of features sets and classifiers. Results up to 97.1% are obtained for classification accuracy between speech of ASD affected children and normal children, using a combination of feature set with SVM classifier. The results are better than other similar few studies. This study should be helpful in developing an automated system for identffying ASD speech, in future.

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Authors
Mohanta, A., & Mittal, V. K.
Affiliations
  • Indian Institute of Information Technology Sri City (IN)
  • Koneru Lakshmaiah Education Foundation (IN)
Venue
2020 National Conference on Communications (NCC)
Publisher
IEEE
Published
2020-02
Pages
1-6
DOI
10.1109/ncc48643.2020.9056084
OpenAlex
W3015641485
Cited by
22
References
39
Citations by year
2026: 1 · 2025: 4 · 2024: 5 · 2023: 2 · 2022: 3 · 2021: 6 · 2020: 1
FWCI
3.91
Type
conference paper
Language
en
Open access
closed
Licence
Locations
National Conference on Communications (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
  • Health Sciences › Medicine › Physiology › Voice and Speech Disorders
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Keywords
autism spectrum disorder, speech classification, acoustic features, fundamental frequency, excitation strength, formants, dominant frequency, zero-crossing rate, support vector machine, ensemble classifiers, children's speech

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