用机器学习方法分析自闭症谱系障碍和唐氏综合征男孩的语音形态和词汇特征

O. Makhnytkina, O. V. Frolova, E. Lyakso
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引用次数: 0

摘要

目的。在本文中,我们提出了一种方法,基于对典型发育男孩(TD)、患有自闭症谱系障碍(ASD)和唐氏综合症(DS)的男孩的语音的形态和词汇特征的比较,来识别他们语音中的显著差异。语言特点是使用形态分析器 pymorphy2 自动提取的。69 名男孩接受了访谈。从每段对话中共提取了 45 个语言特点。曼-惠特尼U检验用于评估语言特征的差异,结果显示,31个TD男孩和ASD男孩的语言特征存在差异,31个TD男孩和DS男孩的语言特征存在差异,15个ASD男孩和DS男孩的语言特征存在差异。这些特征被用于使用梯度提升、随机森林和 AdaBoost 算法等机器学习方法建立分类模型。所识别的特征显示出良好的可分离性,对患有典型发育障碍、自闭症谱系障碍和唐氏综合症的男孩的对话进行分类的准确率达到了 88%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Methods for Analyzing Morphological and Lexical Characteristics of Speech of Boys with Autism Spectrum Disorders and Down Syndrome
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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