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Analysis of Dialogues of Typically Developing Children, Children with Down Syndrome and ASD Using Machine Learning Methods. / Makhnytkina, Olesia; Grigorev, Aleksey; Nikolaev, Aleksander.

Speech and Computer : 23rd International Conference, SPECOM 2021, Proceedings. ред. / Alexey Karpov; Rodmonga Potapova. Springer Nature, 2021. стр. 397-406 (Lecture Notes in Computer Science; Том 12997 LNAI).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование

Harvard

Makhnytkina, O, Grigorev, A & Nikolaev, A 2021, Analysis of Dialogues of Typically Developing Children, Children with Down Syndrome and ASD Using Machine Learning Methods. в A Karpov & R Potapova (ред.), Speech and Computer : 23rd International Conference, SPECOM 2021, Proceedings. Lecture Notes in Computer Science, Том. 12997 LNAI, Springer Nature, стр. 397-406, 23rd International Conference on Speech and Computer, Virtual, Online, Российская Федерация, 27/09/21. https://doi.org/10.1007/978-3-030-87802-3_36

APA

Makhnytkina, O., Grigorev, A., & Nikolaev, A. (2021). Analysis of Dialogues of Typically Developing Children, Children with Down Syndrome and ASD Using Machine Learning Methods. в A. Karpov, & R. Potapova (Ред.), Speech and Computer : 23rd International Conference, SPECOM 2021, Proceedings (стр. 397-406). (Lecture Notes in Computer Science; Том 12997 LNAI). Springer Nature. https://doi.org/10.1007/978-3-030-87802-3_36

Vancouver

Makhnytkina O, Grigorev A, Nikolaev A. Analysis of Dialogues of Typically Developing Children, Children with Down Syndrome and ASD Using Machine Learning Methods. в Karpov A, Potapova R, Редакторы, Speech and Computer : 23rd International Conference, SPECOM 2021, Proceedings. Springer Nature. 2021. стр. 397-406. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-030-87802-3_36

Author

Makhnytkina, Olesia ; Grigorev, Aleksey ; Nikolaev, Aleksander. / Analysis of Dialogues of Typically Developing Children, Children with Down Syndrome and ASD Using Machine Learning Methods. Speech and Computer : 23rd International Conference, SPECOM 2021, Proceedings. Редактор / Alexey Karpov ; Rodmonga Potapova. Springer Nature, 2021. стр. 397-406 (Lecture Notes in Computer Science).

BibTeX

@inproceedings{d8debf09838340b6bf670b862ed3cb7b,
title = "Analysis of Dialogues of Typically Developing Children, Children with Down Syndrome and ASD Using Machine Learning Methods",
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{\textquoteright}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{\textquoteright}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%.",
keywords = "Autism spectrum disorder, Down syndrome, Linguistic features, Machine learning",
author = "Olesia Makhnytkina and Aleksey Grigorev and Aleksander Nikolaev",
note = "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; 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_36",
language = "English",
isbn = "9783030878016",
series = "Lecture Notes in Computer Science",
publisher = "Springer Nature",
pages = "397--406",
editor = "Alexey Karpov and Rodmonga Potapova",
booktitle = "Speech and Computer",
address = "Germany",
url = "http://specom.nw.ru/2021/",

}

RIS

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T1 - Analysis of Dialogues of Typically Developing Children, Children with Down Syndrome and ASD Using Machine Learning Methods

AU - Makhnytkina, Olesia

AU - Grigorev, Aleksey

AU - Nikolaev, Aleksander

N1 - Conference code: 23

PY - 2021/10

Y1 - 2021/10

N2 - 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%.

AB - 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%.

KW - Autism spectrum disorder

KW - Down syndrome

KW - Linguistic features

KW - Machine learning

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UR - https://www.mendeley.com/catalogue/009080eb-c767-3284-abd0-3b0e39536b65/

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DO - 10.1007/978-3-030-87802-3_36

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T3 - Lecture Notes in Computer Science

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EP - 406

BT - Speech and Computer

A2 - Karpov, Alexey

A2 - Potapova, Rodmonga

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

Y2 - 27 September 2021 through 30 September 2021

ER -

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