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

Authors
 HyangHee Kim  ;  Ji-Myoung Choi  ;  Hansaem Kim  ;  Ginju Baek  ;  Bo Seon Kim  ;  Sang Kyu Seo 
Citation
 International Journal of Contents, Vol.15(1) : 39-51, 2019 
Journal Title
 International Journal of Contents 
ISSN
 1738-6764 
Issue Date
2019
Keywords
Aphasia ; Automatic Speech Analysis ; Computational Analysis ; PCA ; Machine Learning
Abstract
Spontaneous 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.
Files in This Item:
T201900948.pdf Download
DOI
10.5392/IJoC.2019.15.1.039
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Rehabilitation Medicine (재활의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Hyang Hee(김향희) ORCID logo https://orcid.org/0000-0003-4949-2512
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/169458
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