This thesis investigates a dynamic programming approach to word hypothesis in the context of a speaker independent, large vocabulary, continuous speech recognition system. Using a method known as Dynamic Time Warping, an undifferentiated phonetic string (one without word boundaries) is parsed to produce all possible words contained in a domain specific lexicon. Dynamic Time Warping is a common method of sequence comparison used in matching the acoustic feature vectors representing an unknown input utterance and some reference utterance. The cumulative least cost path, when compared with some threshold can be used as a decision criterion for recognition. This thesis attempts to extend the DTW technique using strings of phonetic symbols, instead. Three variables that were found to affect the parsing process include: (1) minimum distance threshold, (2) the number of word candidates accepted at any given phonetic index, and (3) the lexical search space used for reference pattern comparisons. The performance of this parser as a function of these variables is discussed. Also discussed is the performance of the parser at a variety of input error conditions.

Library of Congress Subject Headings

Automatic speech recognition; Phonetics, Acoustic--Analysis--Data processing

Publication Date


Document Type


Department, Program, or Center

Computer Science (GCCIS)


Hillenbrand, James


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.S65S44 1993


RIT – Main Campus