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dc.contributor.authorHjelm, Rex Devon
dc.date.accessioned2011-08-30T18:13:37Z
dc.date.available2011-08-30T18:13:37Z
dc.date.issued2011-08-30
dc.date.submittedJuly 2011
dc.identifier.urihttp://hdl.handle.net/1928/13093
dc.description.abstractThis paper explores the applicability of Adaptive Resistance Theory- (ART-) type neural networks for finding and encoding linguistic structures, specifically those corresponding to acoustic patterns in natural speech. We build an interpretation of human perceptual response to acoustic pattern in natural speech, translating this to a neural architecture as a model of acquisition, storage, and classification of acoustic speech patterns.en_US
dc.language.isoen_USen_US
dc.subjectPhoneme Perception Neural Networks Pattern Recognitionen_US
dc.subject.lcshPhonemic awareness
dc.subject.lcshPhonetics, Acoustic
dc.subject.lcshSpeech perception
dc.subject.lcshNeural networks (Neurobiology)
dc.subject.lcshPattern perception
dc.titleA Temporal Fuzzy-ART Neural Network Architecture as a Model of Phoneme Perceptionen_US
dc.typeThesisen_US
dc.description.degreeLinguisticsen_US
dc.description.levelMastersen_US
dc.description.departmentUniversity of New Mexico. Dept. of Linguisticsen_US
dc.description.advisorLuger, George
dc.description.committee-memberMorford, Jill
dc.description.committee-memberCaudell, Thomas


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