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Modeling the Role of Social Informat...

Remirez, Emily Ada.

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Modeling the Role of Social Information in Speech Perception.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling the Role of Social Information in Speech Perception./
Author:	Remirez, Emily Ada.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	159 p.
Notes:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
Subject:	Linguistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31294806
ISBN:	9798384453116

Modeling the Role of Social Information in Speech Perception.
Remirez, Emily Ada.

Modeling the Role of Social Information in Speech Perception. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 159 p.

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2024.

How 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.

ISBN: 9798384453116Subjects--Topical Terms:

524476
Linguistics.
Subjects--Index Terms:

Episodic modeling

Modeling the Role of Social Information in Speech Perception.
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100 1 $a Remirez, Emily Ada. $3 3774826
245 1 0 $a Modeling the Role of Social Information in Speech Perception.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 159 p.
500 $a Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
500 $a Advisor: Johnson, Keith;McGowan, Kevin B.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2024.
520 $a How 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 $a School code: 0028.
650 4 $a Linguistics. $3 524476
650 4 $a Speech therapy. $3 520446
650 4 $a Sociolinguistics. $3 524467
653 $a Episodic modeling
653 $a Exemplar theory
653 $a Generalized context mode
653 $a Speech perception
690 $a 0290
690 $a 0636
690 $a 0460
710 2 $a University of California, Berkeley. $b Linguistics. $3 1672458
773 0 $t Dissertations Abstracts International $g 86-03B.
790 $a 0028
791 $a Ph.D.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31294806