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

Matveev, Yuri; Matveev, Anton; Frolova, Оlga; Lyakso, Elena E.

doi:10.1007/978-3-030-87802-3_38

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…В настоящее время широко обсуждается возможность использования автоматического распознавания психоневрологического состояния ребенка по его речи в качестве одного из методов дополнительной диагностики [Fusaroli et al, 2017;Matveev et al, 2021]. Создается большое количество мобильных приложений для детей, в том числе с АР [Adamu et al, 2019].…”

Section: Introductionunclassified

“…Исследователи выделяют акустические признаки, которые позволяют классифицировать детей на две группы -РАС / ТР [Fusaroli et al, 2017], и ставят задачу определения специфических акустических признаков, позволяющих отличить речь детей с РАС от речи детей с другими диагнозами -умственной отсталостью, смешанными специфическими расстройствами психологического развития, СД [Lyakso et al, 2018;Matveev et al, 2021]. В работе [Cho et al, 2019] впервые была показана возможность совместного применения лингвистических и акустических признаков для автоматического определения методами машинного обучения аутистических расстройств по речи ребенка, ранее в работах рассматривались только акустические признаки [Pokorny et al, 2017].…”

Section: Introductionunclassified

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Machine Learning Methods for Analyzing Morphological and Lexical Characteristics of Speech of Boys with Autism Spectrum Disorders and Down Syndrome

Makhnytkina,

Frolova,

Lyakso

2024

VNSUIP

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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.

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Section: Introductionunclassified

Machine Learning Methods for Analyzing Morphological and Lexical Characteristics of Speech of Boys with Autism Spectrum Disorders and Down Syndrome

Makhnytkina,

Frolova,

Lyakso

2024

VNSUIP

Self Cite

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“…Creating automatic emotion recognition systems in a person's speech is not trivial, especially considering the differences in acoustic features for different genders [6], age groups [7], languages [6,8], cultures [9], and developmental [10] features. For example, in [11], it is reported that the accuracies of speech emotion recognition are "93.3%, 89.4%, and 83.3% for male, female and child utterances respectively".…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Architecture for Children’s Audio–Visual Emotion Recognition

Matveev,

Frolova

et al. 2023

Mathematics

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Detecting and understanding emotions are critical for our daily activities. As emotion recognition (ER) systems develop, we start looking at more difficult cases than just acted adult audio–visual speech. In this work, we investigate the automatic classification of the audio–visual emotional speech of children, which presents several challenges including the lack of publicly available annotated datasets and the low performance of the state-of-the art audio–visual ER systems. In this paper, we present a new corpus of children’s audio–visual emotional speech that we collected. Then, we propose a neural network solution that improves the utilization of the temporal relationships between audio and video modalities in the cross-modal fusion for children’s audio–visual emotion recognition. We select a state-of-the-art neural network architecture as a baseline and present several modifications focused on a deeper learning of the cross-modal temporal relationships using attention. By conducting experiments with our proposed approach and the selected baseline model, we observe a relative improvement in performance by 2%. Finally, we conclude that focusing more on the cross-modal temporal relationships may be beneficial for building ER systems for child–machine communications and environments where qualified professionals work with children.

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Morphological and Emotional Features of the Speech in Children with Typical Development, Autism Spectrum Disorders and Down Syndrome

Makhnytkina

Frolova

Lyakso

2022

Communications in Computer and Information Science

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

Cited by 4 publications

References 22 publications

Machine Learning Methods for Analyzing Morphological and Lexical Characteristics of Speech of Boys with Autism Spectrum Disorders and Down Syndrome

Machine Learning Methods for Analyzing Morphological and Lexical Characteristics of Speech of Boys with Autism Spectrum Disorders and Down Syndrome

A Neural Network Architecture for Children’s Audio–Visual Emotion Recognition

Morphological and Emotional Features of the Speech in Children with Typical Development, Autism Spectrum Disorders and Down Syndrome

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