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
질문

논문 기본 정보

자료유형
학술저널
저자정보
Jungyeon Kim (Yonsei University)
저널정보
한국영어학회 영어학 영어학 Volume.21
발행연도
2021.1
수록면
636 - 655 (20page)

이용수

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

초록· 키워드

오류제보하기
The present study examined the well-known data in which the onset of the third syllable is flapped in capitalistic whereas it is aspirated in militaristic even though both words have the same stress pattern and syllable structure. While a number of studies have considered several different approaches in this discussion including paradigm uniformity effect, foot-based analyses, optimality theoretic (OT) accounts, and analogy, there has been no research that seeks to account for the possibility that the underlying /t/’s of those two words can be realized as both aspirated and flapped by speakers of American English. This study basically follows a prosodic foot-based account to explain this phenomenon and attempts to capture the variant realizations using the audio pronunciation listed in eleven different online dictionaries within the Maximum Entropy (MaxEnt) Model, which is a probabilistic model that assigns each candidate a probability rather than picking a single winner as in standard OT. The frequency data observed from those dictionaries were fed into MaxEnt to see if the learned grammar can successfully predict the observed frequency. The current simulation results show that the frequency found in the actual linguistic data corresponds to that predicted by the training corpus data, which indicates that the learned grammar is able to accurately reproduce the observed frequency. These findings suggest that MaxEnt modeling has a more explanatory power than classical OT analyses in that it can serve to account for grammars involving free variation.

목차

ABSTRACT
1. Introduction
2. Previous Studies
3. Prosodically-Based Account
4. Conclusion
References

참고문헌 (86)

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