Acoustic Feature Design for Speech Recognition, a Statistical Information-Theoretic Approach

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Omar, Mohamed Kamal Mahmoud

Contributors dc:contributor

• Mark Hasegawa-Johnson

• Engineering, Electronics and Electrical

Language dc:language

eng

Identifier

(MiAaPQ)AAI3111625

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/80848