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

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Speech and Computer (SPECOM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12997))

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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%.

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Acknowledgments

The study is financially supported by the Russian Science Foundation (project № 18-18-00063) and the Russian Foundation for Basic Research (project 19-57-45008–IND_a).

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Authors and Affiliations

  1. ITMO University, Saint Petersburg, 197101, Russian Federation

    Olesia Makhnytkina

  2. Saint Petersburg State University, Saint Petersburg, 191124, Russian Federation

    Aleksey Grigorev & Aleksander Nikolaev

Authors
  1. Olesia Makhnytkina
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  2. Aleksey Grigorev
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  3. Aleksander Nikolaev
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Corresponding author

Correspondence to Olesia Makhnytkina .

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Editors and Affiliations

  1. St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia

    Alexey Karpov

  2. Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

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Makhnytkina, O., Grigorev, A., Nikolaev, A. (2021). Analysis of Dialogues of Typically Developing Children, Children with Down Syndrome and ASD Using Machine Learning Methods. In: Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2021. Lecture Notes in Computer Science(), vol 12997. Springer, Cham. https://doi.org/10.1007/978-3-030-87802-3_36

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  • DOI: https://doi.org/10.1007/978-3-030-87802-3_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87801-6

  • Online ISBN: 978-3-030-87802-3

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