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Differentiation of Aphasic Patients from the Normal Control Via a Computational Analysis of Korean Utterances

DC FieldValueLanguage
dc.contributor.author김향희-
dc.date.accessioned2019-05-29T05:12:35Z-
dc.date.available2019-05-29T05:12:35Z-
dc.date.issued2019-
dc.identifier.issn1738-6764-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/169458-
dc.description.abstractSpontaneous speech provides rich information defining the linguistic characteristics of individuals. As such, computational analysis of speech would enhance the efficiency involved in evaluating patients’ speech. This study aims to provide a method to differentiate the persons with and without aphasia based on language usage. Ten aphasic patients and their counterpart normal controls participated, and they were all tasked to describe a set of given words. Their utterances were linguistically processed and compared to each other. Computational analyses from PCA (Principle Component Analysis) to machine learning were conducted to select the relevant linguistic features, and consequently to classify the two groups based on the features selected. It was found that functional words, not content words, were the main differentiator of the two groups. The most viable discriminators were demonstratives, function words, sentence final endings, and postpositions. The machine learning classification model was found to be quite accurate (90%), and to impressively be stable. This study is noteworthy as it is the first attempt that uses computational analysis to characterize the word usage patterns in Korean aphasic patients, thereby discriminating from the normal group.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherThe Korea Contents Association(한국콘텐츠학회)-
dc.relation.isPartOfInternational Journal of Contents-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.titleDifferentiation of Aphasic Patients from the Normal Control Via a Computational Analysis of Korean Utterances-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Rehabilitation Medicine (재활의학교실)-
dc.contributor.googleauthorHyangHee Kim-
dc.contributor.googleauthorJi-Myoung Choi-
dc.contributor.googleauthorHansaem Kim-
dc.contributor.googleauthorGinju Baek-
dc.contributor.googleauthorBo Seon Kim-
dc.contributor.googleauthorSang Kyu Seo-
dc.identifier.doi10.5392/IJoC.2019.15.1.039-
dc.contributor.localIdA01107-
dc.relation.journalcodeJ01102-
dc.identifier.eissn2093-7504-
dc.subject.keywordAphasia-
dc.subject.keywordAutomatic Speech Analysis-
dc.subject.keywordComputational Analysis-
dc.subject.keywordPCA-
dc.subject.keywordMachine Learning-
dc.contributor.alternativeNameKim, Hyang Hee-
dc.contributor.affiliatedAuthor김향희-
dc.citation.volume15-
dc.citation.number1-
dc.citation.startPage39-
dc.citation.endPage51-
dc.identifier.bibliographicCitationInternational Journal of Contents, Vol.15(1) : 39-51, 2019-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Rehabilitation Medicine (재활의학교실) > 1. Journal Papers

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