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Combining speech recognition and speaker verification

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TitleInfo (displayLabel = Citation Title); (type = uniform)
Title
Combining speech recognition and speaker verification
Name (ID = NAME001); (type = personal)
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Mohan
NamePart (type = given)
Aanchan K.
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Aanchan K. Mohan
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author
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Lawrence
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Advisory Committee
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Lawrence R Rabiner
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chair
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Marsic
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Ivan
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Advisory Committee
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Ivan Marsic
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Wilder
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Joseph
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Advisory Committee
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Joseph Wilder
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Rosenberg
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Aaron
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Advisory Committee
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Aaron E Rosenberg
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outside member
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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school
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Text
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theses
OriginInfo
DateCreated (qualifier = exact)
2008
DateOther (qualifier = exact); (type = degree)
2008-10
Language
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English
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electronic
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application/pdf
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text/xml
Extent
xiv, 94 pages
Abstract
Traditional fixed pass-phrase or text-dependent speaker verification systems are vulnerable to replay or spoofing attacks. Random pass-phrase generation, speech verification and text-independent speaker verification could be combined to create a composite speaker verification system, robust to this spoofing problem. This thesis deals with combining speech verification with text-independent speaker verification for this purpose. A method to perform robust, automatic speech verification using a speech recognizer in a forced alignment mode is proposed and evaluated. A text-independent speaker verification system was developed in MATLAB for training and evaluating Gaussian mixture density-based, target speaker and background speaker models. Equal-error rate is the performance metric used in all speaker verification evaluations. To speed up background model training, a simple technique based on sub-sampling or decimating speech frames is presented. Evaluation of two different feature extraction implementations along with an evaluation of the impact on performance of different configurations of the speech features is also carried out. Further, to mitigate problems with reduced training data and to improve performance, Bayesian adaptation of background speaker models with target speaker training data is used to create target speaker models. The performance of these models is evaluated and compared with conventional target speaker models. The impact of the length of test-utterances, variance limiting and the use of training data from multiple recording sessions has also been investigated.
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references (p. 91-94).
Subject (ID = SUBJ1); (authority = RUETD)
Topic
Electrical and Computer Engineering
Subject (ID = SUBJ2); (authority = ETD-LCSH)
Topic
Automatic speech recognition
Subject (ID = SUBJ3); (authority = ETD-LCSH)
Topic
Speech processing systems
Subject (ID = SUBJ4); (authority = ETD-LCSH)
Topic
Identification--Automation
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TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17528
Identifier
ETD_1113
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3959HWW
Genre (authority = ExL-Esploro)
ETD graduate
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The author owns the copyright to this work.
Copyright
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Copyright protected
Availability
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Open
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Name
Aanchan Mohan
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
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Non-exclusive ETD license
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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.
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