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Modeling the Role of Social Information in Speech Perception
Modeling the Role of Social Information in Speech Perception
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151426
- ISBN
- 9798384453116
- DDC
- 401
- 서명/저자
- Modeling the Role of Social Information in Speech Perception
- 발행사항
- [Sl] : University of California, Berkeley, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 159 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Johnson, Keith;McGowan, Kevin B.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2024.
- 초록/해제
- 요약How do we model the relationship between "high level" social constructs and "low level" automatic processing of phonetic detail? Variation in pronunciation is socially informative, and listeners can draw on these social expectations when perceiving speech. This dissertation argues for a closer consideration of variation within sociophonetic exemplar modeling. I do this by reviewing the web of literature, simulating perception events in Python, and conducting an experiment. "Exemplar theory" is a class of models positing that past experiences interpreting stimuli are remembered as exemplars; new stimuli are categorized based on comparison to these stored memories. In particular, I focus on the Generalized Context Model (Nosofsky 1986; Johnson 1997), or GCM. The evidence that social categories, like other higher-order abstractions from stimuli, can play a role in categorization is well-established but loosely unified. Many adopt an episodic or exemplar-based framework in interpreting their results, but focus on the general patterns more than a specific model. I developed a Python library ExemPy which implements the GCM and provides routines for simulating common perception experiment tasks. I suggest applications for both enhancing empirical work and exploring theoretical space. I designed an experiment to explore a key difference among sociophonetic priming literature: whether social expectation is invoked as part of or outside of the phonetic stimulus. Taken together, this work advances an integrative, ecologically informed approach to exemplar-based sociophonetic research, drawing on multiple sources of evidence to contextualize our modeling.
- 일반주제명
- Linguistics
- 일반주제명
- Speech therapy
- 일반주제명
- Sociolinguistics
- 키워드
- Exemplar theory
- 기타저자
- University of California, Berkeley Linguistics
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798384453116
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a401
■1001 ▼aRemirez, Emily Ada.
■24510▼aModeling the Role of Social Information in Speech Perception
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a159 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Johnson, Keith;McGowan, Kevin B.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2024.
■520 ▼aHow do we model the relationship between "high level" social constructs and "low level" automatic processing of phonetic detail? Variation in pronunciation is socially informative, and listeners can draw on these social expectations when perceiving speech. This dissertation argues for a closer consideration of variation within sociophonetic exemplar modeling. I do this by reviewing the web of literature, simulating perception events in Python, and conducting an experiment. "Exemplar theory" is a class of models positing that past experiences interpreting stimuli are remembered as exemplars; new stimuli are categorized based on comparison to these stored memories. In particular, I focus on the Generalized Context Model (Nosofsky 1986; Johnson 1997), or GCM. The evidence that social categories, like other higher-order abstractions from stimuli, can play a role in categorization is well-established but loosely unified. Many adopt an episodic or exemplar-based framework in interpreting their results, but focus on the general patterns more than a specific model. I developed a Python library ExemPy which implements the GCM and provides routines for simulating common perception experiment tasks. I suggest applications for both enhancing empirical work and exploring theoretical space. I designed an experiment to explore a key difference among sociophonetic priming literature: whether social expectation is invoked as part of or outside of the phonetic stimulus. Taken together, this work advances an integrative, ecologically informed approach to exemplar-based sociophonetic research, drawing on multiple sources of evidence to contextualize our modeling.
■590 ▼aSchool code: 0028.
■650 4▼aLinguistics
■650 4▼aSpeech therapy
■650 4▼aSociolinguistics
■653 ▼aEpisodic modeling
■653 ▼aExemplar theory
■653 ▼aGeneralized context mode
■653 ▼aSpeech perception
■690 ▼a0290
■690 ▼a0636
■690 ▼a0460
■71020▼aUniversity of California, Berkeley▼bLinguistics.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161655▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
