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

Olesia Makhnytkina, Aleksey Grigorev, Aleksander Nikolaev

Результат исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование


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%.

Язык оригиналаанглийский
Название основной публикацииSpeech and Computer
Подзаголовок основной публикации23rd International Conference, SPECOM 2021, Proceedings
РедакторыAlexey Karpov, Rodmonga Potapova
ИздательSpringer Nature
ISBN (печатное издание)9783030878016
СостояниеОпубликовано - окт 2021
Событие23rd International Conference on Speech and Computer - Virtual, Online, Российская Федерация
Продолжительность: 27 сен 202130 сен 2021
Номер конференции: 23

Серия публикаций

НазваниеLecture Notes in Computer Science
Том12997 LNAI
ISSN (печатное издание)0302-9743
ISSN (электронное издание)1611-3349


конференция23rd International Conference on Speech and Computer
Сокращенный заголовокSPECOM 2021
Страна/TерриторияРоссийская Федерация
ГородVirtual, Online
Адрес в сети Интернет

Предметные области Scopus

  • Теоретические компьютерные науки
  • Компьютерные науки (все)


Подробные сведения о темах исследования «Analysis of Dialogues of Typically Developing Children, Children with Down Syndrome and ASD Using Machine Learning Methods». Вместе они формируют уникальный семантический отпечаток (fingerprint).