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장애 음성 판별을 위한 의료/전자 융복합 소프트웨어 개발

Development of medical/electrical convergence software for classification between normal and pathological voices

  • 문지혜 (중원대학교 의료공학과) ;
  • 이지연 (중원대학교 의료공학과)
  • Moon, Ji-Hye (Department of Biomedical Engineering, Jungwon University) ;
  • Lee, JiYeoun (Department of Biomedical Engineering, Jungwon University)
  • 투고 : 2015.10.30
  • 심사 : 2015.12.20
  • 발행 : 2015.12.28

초록

장애음성을 판별할 수 있는 소프트웨어가 개발 될 경우, 원격의료와 언어치료 등 여러 융복합 분야에서의 활용도가 매우 높다. 본 논문은 성대 진동에 대한 변화율을 나타내는 의료정보인 음향학적 파라미터와 신호처리 기반 고차 통계량에 기반을 둔 파라미터를 융합하여, CART(Classification And Regression Trees) 분석을 통해서 정상/장애음성 판별 프로그램을 구현하였다. 사용된 음향학적 파라미터는 Jitter(%)와 shimmer(%)이다. 그리고 본 연구에서 제안된 고차통계량 기반 파라미터는 왜도(Skewness)와 첨도(Kurtosis)의 평균과 분산이다. Kay Elemetrics의 데이터베이스에서 무작위로 발췌된 정상음성 53명, 장애 음성 173명의 /아/ 발화를 이용하여 결정트리(Decision tree) 기반장애음성 판별을 위해 평균적으로 83.15%의 성능을 보이는 알고리즘을 구현하였다. 그 결과를 바탕으로 추후 상용화를 고려하여 사용자 친화적인 프레임 워크에 의해 컨텐츠를 생성하는 융복합형 기능이 포함된 장애음성 판별 프로그램을 개발하였다.

If the software is developed to analyze the speech disorder, the application of various converged areas will be very high. This paper implements the user-friendly program based on CART(Classification and regression trees) analysis to distinguish between normal and pathological voices utilizing combination of the acoustical and HOS(Higher-order statistics) parameters. It means convergence between medical information and signal processing. Then the acoustical parameters are Jitter(%) and Shimmer(%). The proposed HOS parameters are means and variances of skewness(MOS and VOS) and kurtosis(MOK and VOK). Database consist of 53 normal and 173 pathological voices distributed by Kay Elemetrics. When the acoustical and proposed parameters together are used to generate the decision tree, the average accuracy is 83.11%. Finally, we developed a program with more user-friendly interface and frameworks.

키워드

참고문헌

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