Linguistics ETDs
Publication Date
7-1-2011
Abstract
This 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.
Language
English
Keywords
Phoneme Perception Neural Networks Pattern Recognition
Document Type
Thesis
Degree Name
Linguistics
Level of Degree
Masters
Department Name
Department of Linguistics
First Committee Member (Chair)
Morford, Jill
Second Committee Member
Caudell, Thomas
Recommended Citation
Hjelm, Rex Devon. "A Temporal Fuzzy-ART Neural Network Architecture as a Model of Phoneme Perception." (2011). https://digitalrepository.unm.edu/ling_etds/17
Comments
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