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Investigating the Corpus Phonetics Pipeline Applied to Diverse Speech Data
Investigating the Corpus Phonetics Pipeline Applied to Diverse Speech Data
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202104643
- ISBN
- 9798288822049
- DDC
- 401
- 서명/저자
- Investigating the Corpus Phonetics Pipeline Applied to Diverse Speech Data
- 발행사항
- [Sl] : University of Washington, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 132 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-01, Section: B.
- 주기사항
- Advisor: Levow, Gina-Anne.
- 학위논문주기
- Thesis (Ph.D.)--University of Washington, 2025.
- 초록/해제
- 요약Corpus phonetics research has become increasingly large-scale as both data and automated tools have become more plentiful and available. Now that there are resources to study more kinds of data, what are some best practices in using these resources, especially when the data is diverse? This dissertation addresses the following research questions: How do we process diverse speech data, and how much can we rely on automated tools to conduct corpus phonetics research? The types of diversity covered in this work include multilingual and fieldwork corpora covering styles including read, spontaneous, and code-switched speech. Across four studies, we show that automated systems in the corpus phonetics pipeline are viable on multilingual and low-resource datasets. We first propose a pipeline that utilizes automated systems that convert orthography to phonemes, model the acoustics and align audio to those phonemes, and extract features for phonetic analysis. We apply this pipeline to a large, multilingual corpus and show both the utility and limitations of this derivative corpus in a careful study of outlying phonetic features. Then, we apply novel techniques to improve the phonetic forced alignment of low-resource field data, a challenging yet important process in language documentation. We encourage the research community to continue developing tools to aid in language documentation and cross-linguistic research. In doing so, it is important to include manual audits and to examine whether or not the tools are genuinely modeling the data.
- 일반주제명
- Linguistics
- 일반주제명
- Computer science
- 일반주제명
- Language arts
- 일반주제명
- Information technology
- 키워드
- Corpus phonetics
- 키워드
- Forced alignment
- 기타저자
- University of Washington Linguistics
- 기본자료저록
- Dissertations Abstracts International. 87-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520260202104643
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■020 ▼a9798288822049
■035 ▼a(MiAaPQ)AAI32114404
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a401
■1001 ▼aProch Ahn, Emily.
■24510▼aInvestigating the Corpus Phonetics Pipeline Applied to Diverse Speech Data
■260 ▼a[Sl]▼bUniversity of Washington▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a132 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-01, Section: B.
■500 ▼aAdvisor: Levow, Gina-Anne.
■5021 ▼aThesis (Ph.D.)--University of Washington, 2025.
■520 ▼aCorpus phonetics research has become increasingly large-scale as both data and automated tools have become more plentiful and available. Now that there are resources to study more kinds of data, what are some best practices in using these resources, especially when the data is diverse? This dissertation addresses the following research questions: How do we process diverse speech data, and how much can we rely on automated tools to conduct corpus phonetics research? The types of diversity covered in this work include multilingual and fieldwork corpora covering styles including read, spontaneous, and code-switched speech. Across four studies, we show that automated systems in the corpus phonetics pipeline are viable on multilingual and low-resource datasets. We first propose a pipeline that utilizes automated systems that convert orthography to phonemes, model the acoustics and align audio to those phonemes, and extract features for phonetic analysis. We apply this pipeline to a large, multilingual corpus and show both the utility and limitations of this derivative corpus in a careful study of outlying phonetic features. Then, we apply novel techniques to improve the phonetic forced alignment of low-resource field data, a challenging yet important process in language documentation. We encourage the research community to continue developing tools to aid in language documentation and cross-linguistic research. In doing so, it is important to include manual audits and to examine whether or not the tools are genuinely modeling the data.
■590 ▼aSchool code: 0250.
■650 4▼aLinguistics
■650 4▼aComputer science
■650 4▼aLanguage arts
■650 4▼aInformation technology
■653 ▼aCorpus phonetics
■653 ▼aForced alignment
■653 ▼aLanguage documentation
■653 ▼aSpeech technology
■653 ▼aDiverse speech data
■690 ▼a0290
■690 ▼a0984
■690 ▼a0279
■690 ▼a0489
■71020▼aUniversity of Washington▼bLinguistics.
■7730 ▼tDissertations Abstracts International▼g87-01B.
■790 ▼a0250
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17358320▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
