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An ideal compensator model of speech perception

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TitleInfo
Title
An ideal compensator model of speech perception
Name (type = personal)
NamePart (type = family)
Knutsen
NamePart (type = given)
Sten Kristian
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Sten Kristian Knutsen
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author
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Kleinschmidt
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Dave
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Dave Kleinschmidt
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Advisory Committee
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chair
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Stromswold
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Karin
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Karin Stromswold
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Advisory Committee
Role
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co-chair
Name (type = personal)
NamePart (type = family)
Hemmer
NamePart (type = given)
Pernille
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Pernille Hemmer
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
co-chair
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
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school
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Text
Genre (authority = marcgt)
theses
Genre (authority = ExL-Esploro)
ETD graduate
OriginInfo
DateCreated (qualifier = exact); (encoding = w3cdtf); (keyDate = yes)
2021
DateOther (type = degree); (qualifier = exact); (encoding = w3cdtf)
2021-01
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
One of the key issues in speech perception is how listeners are able to accurately categorize linguistic units (e.g., phonemes) from acoustic cues that contain variation due to multiple overlapping layers of information (Liberman et al., 1967). Over the years, researchers have developed various compensation procedures (e.g., vowel formant normalization) that strive to overcome this variation and increase classification accuracy. Although computationally efficient and widely used, these compensation procedures fall short conceptually as i) they are not necessarily computational models of compensation/ perception/cognition and ii) they do not allow inferences regarding classification to interact dynamically with inferences regarding compensation. In this work we outline a bayesian computational framework for speech perception and compensation, the ideal compensator. Because our listener model infers how to compensate based on a speaker’s generative model while also simultaneously inferring linguistic category, we believe our approach is novel as it both increases classification accuracy and addresses the conceptual issues ignored by previous compensation models and procedures.
Subject (authority = LCSH)
Topic
Speech perception
Subject (authority = RUETD)
Topic
Psychology
RelatedItem (type = host)
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
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ETD_11397
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application/pdf
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Extent
1 online resource (iii, 57 pages) : illustrations
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
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Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-2t45-xz51
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RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
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Knutsen
GivenName
Sten
Role
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RightsEvent
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Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2021-01-04 16:20:28
AssociatedEntity
Name
Sten Knutsen
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
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License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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2021-01-13T02:40:03
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2021-01-13T02:40:03
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