Analysis of Dialogues of Typically Developing Children, Children with Down Syndrome and ASD Using Machine Learning Methods

Olesia Makhnytkina, Aleksey Grigorev, Aleksander Nikolaev

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

In this paper, we propose an approach for determining significant differences in speech of typically developing children, children with Autism Spectrum Disorder (ASD) and Down syndrome. To start solving this problem, we performed an automatic graphemic and morphological analysis of transcribed children’s dialogues. Sixty-two children (20 children with typical development, 14 with Down syndrome, 28 with autism spectrum disorder) discussed standard set of questions with experimenters; for further analysis, only the children’s replicas were used. A total of 25 linguistic features were extracted from each dialogue: the number of replicas, the number of sentences, the number of tokens, the number of pauses, the number of unfinished words and the part of speech composition. To reduce the dimensionality, we performed Kruskal-Wallis tests to assess differences in these features among the studied groups of children, which allows to select 12 significant features. These features were incorporated into tree models such as Gradient Boosting, Random Forest, Ada Boost. All machine learning methods showed high performance, which allows to conclude about a good differentiating ability of features. Our best method showed a classification accuracy of 83%.

Original languageEnglish
Title of host publicationSpeech and Computer
Subtitle of host publication23rd International Conference, SPECOM 2021, Proceedings
EditorsAlexey Karpov, Rodmonga Potapova
PublisherSpringer Nature
Pages397-406
ISBN (Print)9783030878016
DOIs
StatePublished - Oct 2021
Event23rd International Conference on Speech and Computer, SPECOM 2021 - Virtual, Online, Russian Federation
Duration: 27 Sep 202130 Sep 2021
Conference number: 23
http://specom.nw.ru/2021/

Publication series

NameLecture Notes in Computer Science
Volume12997 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Speech and Computer, SPECOM 2021
Abbreviated titleSPECOM 2021
Country/TerritoryRussian Federation
CityVirtual, Online
Period27/09/2130/09/21
Internet address

Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Autism spectrum disorder
  • Down syndrome
  • Linguistic features
  • Machine learning

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