Abstract
The paper presents the results of automatic classification of the dialogues of Russian-speaking children with typical and atypical development using machine learning methods. The study proposes an approach to developing the automatic system for classification the state of children (typical development, autism spectrum disorders, and Down syndrome) based on the linguistic characteristics of speech. 62 boys aged 8–11 years including 20 children with typical development (TD), 28 with autism spectrum disorders (ASD), and 14 with Down syndrome (DS) were interviewed. The dataset contains 69 files with dialogues between adult and child. Only children’s responses were used for further analysis. Morphological, graphematic, and emotional characteristics of speech were extracted from the text of the dialogues. A total of 62 linguistic features were extracted from each dialogue: the number of replies, sentences, tokens, pauses, and unfinished words; the relative frequencies of parts of speech and some grammatical categories (animacy, number, aspect, involvement, mood, person, tense), and the statistics of positive and negative words use. The Mann-Whitney U test was used to assess differences in the linguistic features of the speech. The differences between boys with TD, ASD, and DS in 40 linguistic features of their speech were revealed. These features were used to develop classification models using machine learning methods: Gradient Boosting, Random Forest, Ada Boost. The revealed features showed a good differentiating ability. The classification accuracy for the dialogues of boys with TD, ASD and DS was 88%.
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The research was financially supported by Russian Science Foundation (project 22-45-02007).
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Makhnytkina, O., Frolova, O., Lyakso, E. (2022). Morphological and Emotional Features of the Speech in Children with Typical Development, Autism Spectrum Disorders and Down Syndrome. In: Malykh, V., Filchenkov, A. (eds) Artificial Intelligence and Natural Language. AINL 2022. Communications in Computer and Information Science, vol 1731. Springer, Cham. https://doi.org/10.1007/978-3-031-23372-2_5
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