Date of Award
12-2006
Document Type
Thesis
Degree Name
Master of Science (MS)
Legacy Department
Electrical Engineering
Committee Chair/Advisor
Gowdy, John N
Abstract
This thesis describes the development of unsupervised neural-based pattern classifiers for the training of vowel pronunciations for students learning a foreign language. This paper examines American learners of the French language. A corpus of single word utterances is compiled from a group of native French speakers. Cepstral features are used to train two unsupervised neural pattern classifiers: a self-organizing map and a growing neural gas. The development and justification for the use of these classifiers is presented. The output from the classifier is translated to a bar graph for visual assessment. The degree to which the utterance sounds native is determined by comparing target graphs and those produced by the user. It is concluded that unsupervised classification techniques can be used to develop a pronunciation training system that is independent of the language used to train the system. This allows for pronunciation training to be easily achieved for low-density languages.
Recommended Citation
Hecker, Joseph, "Unsupervised Neural Pattern Classifiers for Oral Vowel Pronunciation of Foreign-Accented Speech" (2006). All Theses. 24.
https://tigerprints.clemson.edu/all_theses/24