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)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_11397
PhysicalDescription
Form (authority = gmd)
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (iii, 57 pages) : illustrations
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
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
Back to the top
Technical
RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
CreatingApplication
Version
1.4
ApplicationName
macOS Version 11.1 (Build 20C69) Quartz PDFContext