Speech Sciences (음성과학)
- Volume 8 Issue 2
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- Pages.61-71
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- 2001
- /
- 1226-5276(pISSN)
Classification of Pathological Voice from ARS using Neural Network
신경회로망을 이용한 ARS 장애음성의 식별에 관한 연구
- Jo, C.W. ;
- Kim, K.I. ;
- Kim, D.H. ;
-
Kwon, S.B.
;
-
Kim, K.R.
;
- Kim, Y.J. ;
-
Jun, K.R.
;
-
Wang, S.G.
- 조철우 (창원대학교 제어계측공학과) ;
- 김광인 (창원대학교 제어계측공학과) ;
- 김대현 (창원대학교 제어계측공학과) ;
-
권순복
(부산대학교 일반대학원 의공학 협동과정)
;
-
김기련
(부산대학교 일반대학원 의공학 협동과정)
;
- 김용주 (부산대학교 일반대학원 의공학 협동과정) ;
-
전계록
(부산대학교 의과대학 의공학교실)
;
-
왕수건
(부산대학교병원 이비인후과)
- Published : 2001.06.01
Abstract
Speech material, which is collected from ARS(Automatic Response System), was analyzed and classified into disease and non-disease state. The material include 11 different kinds of diseases. Along with ARS speech, DAT(Digital Audio Tape) speech is collected in parallel to give the bench mark. To analyze speech material, analysis tools, which is developed local laboratory, are used to provide an improved and robust performance to the obtained parameters. To classify speech into disease and non-disease class, multi-layered neural network was used. Three different combinations of 3, 6, 12 parameters are tested to obtain the proper network size and to find the best performance. From the experiment, the classification rate of 92.5% was obtained.