Date of Award
Spring 2004
Document Type
Thesis - Restricted
Degree Name
Master of Science (MS)
Department
Electrical and Computer Engineering
First Advisor
Povinelli, Richard J.
Second Advisor
Johnson, Michael T.
Third Advisor
Corliss, George F.
Abstract
A novel method for classification of speech phonemes. based on the combination of dynamical systems theory and filter banks, is introduced. The benefit of this approach is seen in its ability to model nonlinear characteristics of speech, something that traditional methods cannot do. The modeling tool that provides this capability is the reconstructed phase space. This space carries all the dynamical information present in the signal's underlying system. The reconstructed phase spaces used for modeling and classification of the phonemes are built using frequency sub-banded signals that are generated using a set of band-pass filters. This approach is motivated by empirical evidence that suggests humans process and recognize speech in sub-bands. Modeling and classification is performed on the sub-banded reconstructed phase spaces using Gaussian Mixture Models, and the results of the classifications for each sub-band are combined to form an overall classification. Several methods for the combination of the sub-band classifications are examined, and it is found that an un-weighted linear combination produces classification accuracies that are significantly higher than those of a classification system using reconstructed phase spaces of unfiltered signals. Results also demonstrate that the proposed phoneme classification system is competitive with state-of-the-art approaches.
Recommended Citation
Indrebo, Kevin Michael, "Sub-banded Reconstructed Phase Spaces for Speech Recognition" (2004). Master's Theses (1922-2009) Access restricted to Marquette Campus. 4878.
https://epublications.marquette.edu/theses/4878