TY - JOUR

T1 - Методы машинного обучения для анализа морфологических и лексических особенностей речи мальчиков с расстройствами аутистического спектра и синдромом Дауна

AU - Махныткина, Олеся Владимировна

AU - Фролова, Ольга Владимировна

AU - Ляксо, Елена Евгеньевна

PY - 2024/2/21

Y1 - 2024/2/21

N2 - Purpose . In this paper, we propose an approach to identifying significant differences in the speech of typically developing boys (TD), boys with Autism Spectrum Disorder (ASD) and Down syndrome (DS) based on a comparison of morphological and lexical characteristics of their speech. The linguistic characteristics were extracted automatically using the morphological analyzer pymorphy2. Sixty nine boys were interviewed. In total, 45 linguistic features were extracted from each dialogue. Results . The Mann – Whitney U test was used for assessing the differences in linguistic features of speech, and differences were identified for 31 linguistic features of speech of boys with TD and with ASD, 31 linguistic features of speech of boys with TD and with DS, and 15 linguistic features of speech of boys with ASD and with DS. These features were used to build classification models using machine learning methods: gradient boosting, random forest, and AdaBoost algorithm. The identified features showed good separability, and the accuracy of the classification of the dialogues of boys with typical development, autism spectrum disorders and Down syndrome equal to 88 % was achieved.

AB - Purpose . In this paper, we propose an approach to identifying significant differences in the speech of typically developing boys (TD), boys with Autism Spectrum Disorder (ASD) and Down syndrome (DS) based on a comparison of morphological and lexical characteristics of their speech. The linguistic characteristics were extracted automatically using the morphological analyzer pymorphy2. Sixty nine boys were interviewed. In total, 45 linguistic features were extracted from each dialogue. Results . The Mann – Whitney U test was used for assessing the differences in linguistic features of speech, and differences were identified for 31 linguistic features of speech of boys with TD and with ASD, 31 linguistic features of speech of boys with TD and with DS, and 15 linguistic features of speech of boys with ASD and with DS. These features were used to build classification models using machine learning methods: gradient boosting, random forest, and AdaBoost algorithm. The identified features showed good separability, and the accuracy of the classification of the dialogues of boys with typical development, autism spectrum disorders and Down syndrome equal to 88 % was achieved.

UR - https://www.mendeley.com/catalogue/0f239edf-c980-3c9f-bb28-7e0fe0ef9713/

U2 - 10.25205/1818-7919-2024-23-2-39-55

DO - 10.25205/1818-7919-2024-23-2-39-55

M3 - статья

VL - 23

SP - 39

EP - 55

JO - Vestnik Novosibirskogo Gosudarstvennogo Universiteta, Seriya: Istoriya, Filologiya

JF - Vestnik Novosibirskogo Gosudarstvennogo Universiteta, Seriya: Istoriya, Filologiya

SN - 1818-7919

IS - 2

ER -