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On the use of frame and segment-based methods for the detection and classification of speech sounds and features

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Text
TitleInfo (ID = T-1)
Title
On the use of frame and segment-based methods for the detection and classification of speech sounds and features
SubTitle
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PartNumber
NonSort
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ETD_2193
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051833
Language (objectPart = )
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eng
Genre (authority = marcgt)
theses
Subject (ID = SBJ-1); (authority = RUETD)
Topic
Electrical and Computer Engineering
Subject (ID = SBJ-2); (authority = ETD-LCSH)
Topic
Speech processing systems
Abstract
Statistical data-driven methods and knowledge-based methods are two recent trends in Automatic Speech Recognition (ASR). Hidden Markov Model (HMM)-based speech recognition techniques have achieved great success for controlled tasks and environments. However, when we require improved accuracy and robustness (closer to Human Speech Recognition (HSR)), HMM algorithms for speech recognition gradually fail. Hence a need has emerged to incorporate higher level linguistic information into ASR systems in order to further discriminate between speech classes or phonemes with high confusion rates. The Automatic Speech Attribute Transcription (ASAT) project is one of the recent research efforts that has tried to bridge the gap between ASR and HSR.
In this thesis we focus on the design and optimization of the front end processing of the ASAT system, whose goal is to estimate a set of attribute and phoneme probability lattices which can be combined with information from higher level knowledge sources in a set of speech event verification modules in order to make a final recognition decision.
We propose a set of both frame-based methods and segment-based methods to improve the recognition performance of distinctive features and phonemes in English. We also study and evaluate both a parallel speech feature organization and a hierarchical phoneme topology. There are 4 main parts in this thesis work. In the first part, we use frame-based methods to estimate the likelihood of static sounds (e.g., steady vowels, fricatives, etc), and implement the parallel feature detection using Multi-Layer Perceptrons (MLPs) in order to detect the 14 Sound Pattern of English (SPE) features. In the second part, we use segment-based methods to classify dynamic sounds (e.g., stop consonants, diphthongs, etc), and use Time-Delay Neural Networks (TDNNs) to recognize phoneme classes in a hierarchical phoneme and feature organization. In the third part and in the forth part, we combine the frame-based parallel speech feature detection system and the segment-based hierarchical phoneme classification system to improve the overall phoneme classification performance and the speech feature detection performance.
The main contribution of this thesis is the creation of a phoneme recognizer that overcomes the disadvantages of pure statistical or knowledge-based systems, and provides a way to incorporate acoustic/phonetic/linguistic knowledge into an existing (HMM-based) automatic speech recognition system.
PhysicalDescription
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electronic resource
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xiv, 127 p. : ill.
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Ph.D.
Note (type = bibliography)
Includes bibliographical references (p. 121-126)
Note (type = statement of responsibility)
by Jun Hou
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Hou
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Jun
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1974-
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Jun Hou
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Lawrence
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Lawrence R Rabiner
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Marsic
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Ivan
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Ivan Marsic
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Wilder
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Joseph
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Joseph Wilder
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Rosenberg
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Aaron
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Aaron Rosenberg
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Lee
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Chin-Hui
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Chin-Hui Lee
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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
OriginInfo
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2009
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2009-10
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xx
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Rutgers University Electronic Theses and Dissertations
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ETD
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3P26Z9W
Genre (authority = ExL-Esploro)
ETD doctoral
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The author owns the copyright to this work
Copyright
Status
Copyright protected
Notice
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Availability
Status
Open
Reason
Permission or license
Note
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Hou
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Jun
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Jun Hou
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Rutgers University. Graduate School - New Brunswick
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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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