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Automatic Recognition of the Psychoneurological State of Children: Autism Spectrum Disorders, Down Syndrome, Typical Development

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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 explore the problem of automatic recognition of psychoneurological states: Autism Spectrum Disorders, Down Syndrome, Typical Development of 7–10 years old children from their speech in the Russian language. We described the results of fully automatic recognition based on our proprietary speech dataset. Along with SVM, we used the ComParE features from Computational Paralinguistic Challenges. The results on our dataset showed high performance of automated recognition of psychoneurological states of 7–10 years old children from their speech. The results are theoretically and practically valuable, they will expand the knowledge about human voice uniqueness, possibilities of diagnostics of human psychoneurological states by voice and speech features, and creation of alternative communicative systems.

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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, St. Petersburg, Russia

    Yuri Matveev & Anton Matveev

  2. Saint Petersburg State University, St. Petersburg, Russia

    Olga Frolova & Elena Lyakso

Authors
  1. Yuri Matveev
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  2. Anton Matveev
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  3. Olga Frolova
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  4. Elena Lyakso
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Editor information

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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Matveev, Y., Matveev, A., Frolova, O., Lyakso, E. (2021). Automatic Recognition of the Psychoneurological State of Children: Autism Spectrum Disorders, Down Syndrome, Typical Development. 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_38

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  • DOI: https://doi.org/10.1007/978-3-030-87802-3_38

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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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