In continuous speech, the identification of phonemes requires the ability to extract features that are capable of characterizing the acoustic signal. Previous work has shown that relatively high classification accuracy can be obtained from a single spectrum taken during the steady-state portion of the phoneme, assuming that the phonetic environment is held constant. The present study represents an attempt to extend this work to variable phonetic contexts by using dynamic rather than static spectral information. This thesis has four aims: 1) Classify vowels in continuous speech; 2) Find the optimal set of features that best describe the vowel regions; 3) Compare the classification results using a multivariate maximum likelihood distance measure with those of a neural network using the backpropagation model; 4) Examine the classification performance of a Hidden Markov Model given a pathway through phonetic space.

Library of Congress Subject Headings

Automatic speech recognitionSpeech processing systems--Design and construction; Pattern recognition systems--Design and construction; Vowels--Analysis--Data processing

Publication Date


Document Type


Department, Program, or Center

Computer Science (GCCIS)


Hillenbrand, James

Advisor/Committee Member

Biles, John

Advisor/Committee Member

Anderson, Peter


Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works. Physical copy available through RIT's The Wallace Library at: TK7882.S65 S725 1989


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