Open AccessA Bayesian Model for Prediction of Stroke with Voice Onset Time
Author(s) -
Seung Nam Min,
Se Jin Park,
Jung Nam Im,
Murali Subramaniyam
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/912/6/062003
Subject(s) - stroke (engine) , voice onset time , bayesian probability , standard deviation , audiology , pronunciation , speech recognition , statistics , medicine , computer science , mathematics , engineering , mechanical engineering , linguistics , philosophy , voice
The purpose of this study was to examine the changes in voice onset time (VOT) of stroke patients (elderly) and healthy elderly, and to compare them. Also, to propose a prediction model by considering speech analysis data. One hundred and fifty-three healthy elderly and fourty six stroke patients participated in this research study. Each group performed a plosive pronunciation of a Korean word 3 times, and voice signals were recorded. The speech analysis calculates probability parameters of speech signals. The parameters were mean, standard deviation, minimum value, and maximum value of the voice onset time. Finally, a Bayesian model was prepared with these parameters as inputs to predict stroke. Both groups’ speech signals were analyzed and confirmed that there were significantly different in their VOT parameters. And with the calculated probability of both stroke and healthy elderly, the Bayesian prediction model was proposed for stroke prediction. This present study shows that the proposed prediction model could assist in classifying whether the person having stroke or not through their voice onset time data.