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
{"title":"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","authors":"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","doi":"10.1016/j.csl.2024.101665","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":50638,"journal":{"name":"Computer Speech and Language","volume":"89 ","pages":"Article 101665"},"PeriodicalIF":3.1000,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0885230824000482/pdfft?md5=6554015220341426a1f33615cd53fd75&pid=1-s2.0-S0885230824000482-main.pdf","citationCount":"0","resultStr":"{\"title\":\"An exploratory characterization of speech- and fine-motor coordination in verbal children with Autism spectrum disorder\",\"authors\":\"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\",\"doi\":\"10.1016/j.csl.2024.101665\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":50638,\"journal\":{\"name\":\"Computer Speech and Language\",\"volume\":\"89 \",\"pages\":\"Article 101665\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0885230824000482/pdfft?md5=6554015220341426a1f33615cd53fd75&pid=1-s2.0-S0885230824000482-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Speech and Language\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0885230824000482\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Speech and Language","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0885230824000482","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
An exploratory characterization of speech- and fine-motor coordination in verbal children with Autism spectrum disorder
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.
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.