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Title:Acoustic Feature Design for Speech Recognition, a Statistical Information-Theoretic Approach
Author(s):Omar, Mohamed Kamal Mahmoud
Doctoral Committee Chair(s):Mark Hasegawa-Johnson
Department / Program:Electrical Engineering
Discipline:Electrical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Engineering, Electronics and Electrical
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.
Issue Date:2003
Description:148 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2003.
Other Identifier(s):(MiAaPQ)AAI3111625
Date Available in IDEALS:2015-09-25
Date Deposited:2003

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