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Automatic Recognition of the Psychoneurological State of Children : Autism Spectrum Disorders, Down Syndrome, Typical Development. / Matveev, Yuri; Matveev, Anton; Frolova, Olga; Lyakso, Elena.

Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings. ed. / Alexey Karpov; Rodmonga Potapova. Vol. 12997 Springer Nature, 2021. p. 417-425 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12997 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

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 A Karpov & R Potapova (eds), Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings. vol. 12997, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12997 LNAI, Springer Nature, pp. 417-425, 23rd International Conference on Speech and Computer, SPECOM 2021, Virtual, Online, Russian Federation, 27/09/21. https://doi.org/10.1007/978-3-030-87802-3_38

APA

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 A. Karpov, & R. Potapova (Eds.), Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings (Vol. 12997, pp. 417-425). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12997 LNAI). Springer Nature. https://doi.org/10.1007/978-3-030-87802-3_38

Vancouver

Matveev Y, Matveev A, Frolova O, Lyakso E. Automatic Recognition of the Psychoneurological State of Children: Autism Spectrum Disorders, Down Syndrome, Typical Development. In Karpov A, Potapova R, editors, Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings. Vol. 12997. Springer Nature. 2021. p. 417-425. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-87802-3_38

Author

Matveev, Yuri ; Matveev, Anton ; Frolova, Olga ; Lyakso, Elena. / Automatic Recognition of the Psychoneurological State of Children : Autism Spectrum Disorders, Down Syndrome, Typical Development. Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings. editor / Alexey Karpov ; Rodmonga Potapova. Vol. 12997 Springer Nature, 2021. pp. 417-425 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{67b0a337b92c4888ad8935eb7dbfe37f,
title = "Automatic Recognition of the Psychoneurological State of Children: Autism Spectrum Disorders, Down Syndrome, Typical Development",
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.",
keywords = "Autism spectrum disorders, Automatic recognition, Down syndrome, Psychoneurological state",
author = "Yuri Matveev and Anton Matveev and Olga Frolova and Elena Lyakso",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 23rd International Conference on Speech and Computer, SPECOM 2021 ; Conference date: 27-09-2021 Through 30-09-2021",
year = "2021",
month = oct,
doi = "10.1007/978-3-030-87802-3_38",
language = "English",
isbn = "9783030878016",
volume = "12997",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "417--425",
editor = "Alexey Karpov and Rodmonga Potapova",
booktitle = "Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings",
address = "Germany",
url = "http://specom.nw.ru/2021/",

}

RIS

TY - GEN

T1 - Automatic Recognition of the Psychoneurological State of Children

T2 - 23rd International Conference on Speech and Computer, SPECOM 2021

AU - Matveev, Yuri

AU - Matveev, Anton

AU - Frolova, Olga

AU - Lyakso, Elena

N1 - Conference code: 23

PY - 2021/10

Y1 - 2021/10

N2 - 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.

AB - 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.

KW - Autism spectrum disorders

KW - Automatic recognition

KW - Down syndrome

KW - Psychoneurological state

UR - http://www.scopus.com/inward/record.url?scp=85116388585&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/41aec48f-7cbb-3adb-aa3c-a6d86022eb3b/

U2 - 10.1007/978-3-030-87802-3_38

DO - 10.1007/978-3-030-87802-3_38

M3 - Conference contribution

AN - SCOPUS:85116388585

SN - 9783030878016

VL - 12997

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 417

EP - 425

BT - Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings

A2 - Karpov, Alexey

A2 - Potapova, Rodmonga

PB - Springer Nature

Y2 - 27 September 2021 through 30 September 2021

ER -

ID: 87318380