Acoustic Feature Design for Speech Recognition, a Statistical Information-Theoretic Approach
Omar, Mohamed Kamal Mahmoud
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https://hdl.handle.net/2142/80848
Description
Title
Acoustic Feature Design for Speech Recognition, a Statistical Information-Theoretic Approach
Author(s)
Omar, Mohamed Kamal Mahmoud
Issue Date
2003
Doctoral Committee Chair(s)
Mark Hasegawa-Johnson
Department of Study
Electrical Engineering
Discipline
Electrical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Electronics and Electrical
Language
eng
Abstract
In the second part of this work we present a generalization of linear discriminant analysis (LDA) that optimizes a discriminative criterion and solves the problem in the lower-dimensional subspace. We start with showing that the calculation of the LDA projection matrix is a maximum mutual information estimation problem in the lower-dimensional space with some constraints on the model of the joint conditional and unconditional PDFs of the features, and then, by relaxing these constraints, we develop a dimensionality reduction approach that maximizes the conditional mutual information between the class identity and the feature vector in the lower-dimensional space given the recognizer model.
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