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University of New Mexico
A Temporal Fuzzy-ART Neural Network Architecture as a Model of Phoneme Perception
Abstract
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.
Degree
thesis:*- Name thesis:degree_name
- Linguistics
- Level thesis:degree_level
- Thesis
- Discipline thesis:degree_discipline
- Department of Linguistics
- Year
- 2011
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Hjelm, Rex Devon
- Contributors dc:contributor
-
- Luger, George
- Morford, Jill
- Caudell, Thomas
Subjects
dc:subject × 1Rights
- Language dc:language
- English
Identifiers
dc:identifier.*- Repository record dc:identifier
- https://digitalrepository.unm.edu/ling_etds/17
- OAI identifier oai:identifier
- oai:digitalrepository.unm.edu:ling_etds-1016