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

논문 기본 정보

자료유형
학술저널
저자정보
저널정보
한국영미어문학회 영미어문학 영미어문학 제137호
발행연도
2020.1
수록면
241 - 258 (18page)

이용수

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

초록· 키워드

오류제보하기
This study aims to investigate how vowels are categorized into a given number of clusters using an unsupervised machine learning technique called the k-means clustering algorithm. The results of matching clusters to vowel types can explain to what extent the categorizations of vowels are based purely on parameters obtained from the data. The parameters used for the clustering are the first and the second formants in bark. The dataset used in this study is from Hillenbrand et al. (1995). The target vowels were restricted to those produced by adult males. The k was increased stepwise from 3 to 9 to see what phonological features appeared to be active in separating a new cluster from the originating cluster. It was found that the feature [±back] is one of the primary features that differentiates vowel classes and the feature [±high] is used in subsequent separations of the vowel classes. It was also found that high confusability in the perception of vowels is attributable to overlapping distributions of the samples. Vowel duration is critical in clarifying /ɔ/ and /ʌ/, and /ɛ/ and /æ/ as was reported in Hillendbrand et al. (2000). In our study, /æ/ was the second last and /ɔ/ was the last cluster separated from /ɛ, æ/ and /ʌ, ɔ/ clusters, respectively.

목차

등록된 정보가 없습니다.

참고문헌 (0)

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