메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

이 논문의 연구 히스토리 (3)

초록· 키워드

오류제보하기
Background and Objectives The purpose of this study was to conduct a preliminary study to develop a standardized guideline for terminology necessary to describe auditory perception qualitatively and quantitatively in voice disorders in the Korean language. Subjects and Method At first, a subjective questionnaire for Korean translation and definition of 25 auditory perceptual assessment terms proposed by Titze was answered by six speech-language pathologists. Secondly, a new questionnaire that was reconstructed with objective items based on the responses to the first questionnaire was completed by 14 experts who had experience on voice disorder for more than 10 years in Korea. In both questionnaires, the necessity of 32 auditory perceptual assessment terms selected from the 25 terms defined by Titze, GRBAS and CAPE-V was surveyed. Results The consensus on the Korean translation of auditory perceptual assessment terms was moderate (52.6%) between experts. The terms ‘rough’ and ‘shimmer’ demonstrated highest consensus of 85.7%. The consensus on Korean definition of auditory perceptual assessment terms was also moderate (61.6%). The term ‘yawny’ showed complete consensus (100%). The necessity of auditory perceptual assessment terms varied with terms, but showed high consensus. Conclusion It is necessary to develop and apply various standardized vocabulary terms for clinical evaluation so that they can express pathology and physiological characteristics during vocalization. In addition, despite the fact that there are a lot of auditory-perceptual terms, there is a lack of knowledge about voice quality terms, suggesting the need to promote and educate the developed voice quality terms. Korean J Otorhinolaryngol-Head Neck Surg 2017;60(12):653-63

목차

등록된 정보가 없습니다.

참고문헌 (14)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0