상세정보
Export to Refworks부가기능
Cross-Linguistic Acoustic Characteristics of Phonation: A Machine Learning Approach
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Cross-Linguistic Acoustic Characteristics of Phonation: A Machine Learning Approach. |
| 개인저자 | Panfili, Laura Maggia. |
| 단체저자명 | University of Washington. Linguistics. |
| 발행사항 | [S.l.]: University of Washington., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 360 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12A(E). Dissertation Abstract International |
| ISBN | 9780438175389 |
| 학위논문주기 | Thesis (Ph.D.)--University of Washington, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: A.
Adviser: Richard Wright. |
| 요약 | Phonation, the process of producing a quasi-periodic sound wave through vocal fold vibration, plays different roles in different languages. Phonation types, or voice qualities, are produced by adjusting the length, thickness, and separation of t |
| 요약 | This study examines phonation in six languages from four families: English, Gujarati, Hmong, Mandarin, Mazatec, and Zapotec. These languages use phonation in a variety of ways, including contrastively, alongside tones, sociolinguistically, allop |
| 요약 | Machine learning was also used to fine tune a classifier for English phonation types. Unlike other voice quality classifiers, this study focuses on just English and on the three-way breathy vs. modal vs. creaky contrast, rather than on a binary |
| 요약 | This dissertation demonstrates that machine learning is a powerful tool for the study of phonation. It illuminates some of the previously unexamined similarities and differences between phonation types in different languages, and introduces a ne |
| 일반주제명 | Linguistics. Artificial intelligence. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
