An exploratory characterization of speech- and fine-motor coordination in verbal children with Autism spectrum disorder

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Tanya Talkar , James R. Williamson , Sophia Yuditskaya , Daniel J. Hannon , Hrishikesh M. Rao , Lisa Nowinski , Hannah Saro , Maria Mody , Christopher J. McDougle , Thomas F. Quatieri
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Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder often associated with difficulties in speech production and fine-motor tasks. Thus, there is a need to develop objective measures to assess and understand speech production and other fine-motor challenges in individuals with ASD. In addition, recent research suggests that difficulties with speech production and fine-motor tasks may contribute to language difficulties in ASD. In this paper, we explore the utility of an off-body recording platform, from which we administer a speech- and fine-motor protocol to verbal children with ASD and neurotypical controls. We utilize a correlation-based analysis technique to develop proxy measures of motor coordination from signals derived from recordings of speech- and fine-motor behaviors. Eigenvalues of the resulting correlation matrix are inputs to Gaussian Mixture Models to discriminate between highly-verbal children with ASD and neurotypical controls. These eigenvalues also characterize the complexity (underlying dimensionality) of representative signals of speech- and fine-motor movement dynamics, and form the feature basis to estimate scores on an expressive vocabulary measure. Based on a pilot dataset (15 ASD and 15 controls), features derived from an oral story reading task are used in discriminating between the two groups with AUCs > 0.80, and highlight lower complexity of coordination in children with ASD. Features derived from handwriting and maze tracing tasks led to AUCs of 0.86 and 0.91, however features derived from ocular tasks did not aid in discrimination between the ASD and neurotypical groups. In addition, features derived from free speech and sustained vowel tasks are strongly correlated with expressive vocabulary scores. These results indicate the promise of a correlation-based analysis in elucidating motor differences between individuals with ASD and neurotypical controls.

自闭症谱系障碍言语儿童言语和精细动作协调性的特征探索
自闭症谱系障碍(ASD)是一种神经发育障碍,通常与言语表达和精细运动任务方面的困难有关。因此,有必要制定客观的测量方法,以评估和了解自闭症谱系障碍患者在言语表达和其他精细动作方面遇到的困难。此外,最近的研究表明,言语生成和精细运动任务方面的困难可能会导致 ASD 患者的语言障碍。在本文中,我们探索了离体记录平台的实用性,并通过该平台对患有 ASD 的言语儿童和神经正常对照组儿童实施了语言和精细运动协议。我们利用基于相关性的分析技术,从语言和精细运动行为的记录信号中开发出运动协调性的替代测量方法。所得相关矩阵的特征值是高斯混合模型的输入,用于区分高度言语障碍儿童和神经畸形对照组儿童。这些特征值还表征了语言和精细运动动态代表性信号的复杂性(基本维度),并构成了估算表达性词汇量得分的特征基础。基于试验数据集(15 名 ASD 患儿和 15 名对照组患儿),来自口头故事阅读任务的特征用于区分两组患儿,其 AUCs > 为 0.80,并突出显示了 ASD 患儿较低的协调复杂性。从手写和迷宫追踪任务中得出的特征的AUC分别为0.86和0.91,但从眼部任务中得出的特征并不能帮助区分ASD组和神经畸形组。此外,从自由言语和持续元音任务中得出的特征与表达词汇得分密切相关。这些结果表明,基于相关性的分析有望阐明 ASD 患者与神经畸形对照组之间的运动差异。
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来源期刊
Computer Speech and Language
Computer Speech and Language 工程技术-计算机:人工智能
CiteScore
11.30
自引率
4.70%
发文量
80
审稿时长
22.9 weeks
期刊介绍: Computer Speech & Language publishes reports of original research related to the recognition, understanding, production, coding and mining of speech and language. The speech and language sciences have a long history, but it is only relatively recently that large-scale implementation of and experimentation with complex models of speech and language processing has become feasible. Such research is often carried out somewhat separately by practitioners of artificial intelligence, computer science, electronic engineering, information retrieval, linguistics, phonetics, or psychology.
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