Phonetic acquisition in cortical dynamics, a computational approach

Autores
Dematties, Dario Jesus; Rizzi, Silvio; Thiruvathukal, George; Wainselboim, Alejandro Javier; Zanutto, Bonifacio Silvano
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Many computational theories have been developed to improve artificial phonetic classificationperformance from linguistic auditory streams. However, less attention has been given topsycholinguistic data and neurophysiological features recently found in cortical tissue. Wefocus on a context in which basic linguistic units?such as phonemes?are extracted androbustly classified by humans and other animals from complex acoustic streams in speechdata. We are especially motivated by the fact that 8-month-old human infants can accomplishsegmentation of words from fluent audio streams based exclusively on the statisticalrelationships between neighboring speech sounds without any kind of supervision. In thispaper, we introduce a biologically inspired and fully unsupervised neurocomputationalapproach that incorporates key neurophysiological and anatomical cortical properties,including columnar organization, spontaneous micro-columnar formation, adaptation to contextualactivations and Sparse Distributed Representations (SDRs) produced by means ofpartial N-Methyl-D-aspartic acid (NMDA) depolarization. Its feature abstraction capabilitiesshow promising phonetic invariance and generalization attributes. Our model improves theperformance of a Support Vector Machine (SVM) classifier for monosyllabic, disyllabic andtrisyllabic word classification tasks in the presence of environmental disturbances such aswhite noise, reverberation, and pitch and voice variations. Furthermore, our approachemphasizes potential self-organizing cortical principles achieving improvement without anykind of optimization guidance which could minimize hypothetical loss functions by meansof?for example?backpropagation. Thus, our computational model outperforms multiresolutionspectro-temporal auditory feature representations using only the statistical sequentialstructure immerse in the phonotactic rules of the input stream.
Fil: Dematties, Dario Jesus. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina
Fil: Rizzi, Silvio. Argonne National Laboratory; Estados Unidos
Fil: Thiruvathukal, George. Loyola University; Estados Unidos
Fil: Wainselboim, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; Argentina
Fil: Zanutto, Bonifacio Silvano. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina
Materia
Phonetic acquisition
Cortical dynamics
Neurobiological plausibility
Neural networks
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/124688

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network_name_str CONICET Digital (CONICET)
spelling Phonetic acquisition in cortical dynamics, a computational approachDematties, Dario JesusRizzi, SilvioThiruvathukal, GeorgeWainselboim, Alejandro JavierZanutto, Bonifacio SilvanoPhonetic acquisitionCortical dynamicsNeurobiological plausibilityNeural networkshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Many computational theories have been developed to improve artificial phonetic classificationperformance from linguistic auditory streams. However, less attention has been given topsycholinguistic data and neurophysiological features recently found in cortical tissue. Wefocus on a context in which basic linguistic units?such as phonemes?are extracted androbustly classified by humans and other animals from complex acoustic streams in speechdata. We are especially motivated by the fact that 8-month-old human infants can accomplishsegmentation of words from fluent audio streams based exclusively on the statisticalrelationships between neighboring speech sounds without any kind of supervision. In thispaper, we introduce a biologically inspired and fully unsupervised neurocomputationalapproach that incorporates key neurophysiological and anatomical cortical properties,including columnar organization, spontaneous micro-columnar formation, adaptation to contextualactivations and Sparse Distributed Representations (SDRs) produced by means ofpartial N-Methyl-D-aspartic acid (NMDA) depolarization. Its feature abstraction capabilitiesshow promising phonetic invariance and generalization attributes. Our model improves theperformance of a Support Vector Machine (SVM) classifier for monosyllabic, disyllabic andtrisyllabic word classification tasks in the presence of environmental disturbances such aswhite noise, reverberation, and pitch and voice variations. Furthermore, our approachemphasizes potential self-organizing cortical principles achieving improvement without anykind of optimization guidance which could minimize hypothetical loss functions by meansof?for example?backpropagation. Thus, our computational model outperforms multiresolutionspectro-temporal auditory feature representations using only the statistical sequentialstructure immerse in the phonotactic rules of the input stream.Fil: Dematties, Dario Jesus. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; ArgentinaFil: Rizzi, Silvio. Argonne National Laboratory; Estados UnidosFil: Thiruvathukal, George. Loyola University; Estados UnidosFil: Wainselboim, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; ArgentinaFil: Zanutto, Bonifacio Silvano. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaPublic Library of Science2019-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/124688Dematties, Dario Jesus; Rizzi, Silvio; Thiruvathukal, George; Wainselboim, Alejandro Javier; Zanutto, Bonifacio Silvano; Phonetic acquisition in cortical dynamics, a computational approach; Public Library of Science; Plos One; 14; 6; 6-2019; 1-281932-6203CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0217966info:eu-repo/semantics/altIdentifier/url/https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0217966info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:06:13Zoai:ri.conicet.gov.ar:11336/124688instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:06:14.032CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Phonetic acquisition in cortical dynamics, a computational approach
title Phonetic acquisition in cortical dynamics, a computational approach
spellingShingle Phonetic acquisition in cortical dynamics, a computational approach
Dematties, Dario Jesus
Phonetic acquisition
Cortical dynamics
Neurobiological plausibility
Neural networks
title_short Phonetic acquisition in cortical dynamics, a computational approach
title_full Phonetic acquisition in cortical dynamics, a computational approach
title_fullStr Phonetic acquisition in cortical dynamics, a computational approach
title_full_unstemmed Phonetic acquisition in cortical dynamics, a computational approach
title_sort Phonetic acquisition in cortical dynamics, a computational approach
dc.creator.none.fl_str_mv Dematties, Dario Jesus
Rizzi, Silvio
Thiruvathukal, George
Wainselboim, Alejandro Javier
Zanutto, Bonifacio Silvano
author Dematties, Dario Jesus
author_facet Dematties, Dario Jesus
Rizzi, Silvio
Thiruvathukal, George
Wainselboim, Alejandro Javier
Zanutto, Bonifacio Silvano
author_role author
author2 Rizzi, Silvio
Thiruvathukal, George
Wainselboim, Alejandro Javier
Zanutto, Bonifacio Silvano
author2_role author
author
author
author
dc.subject.none.fl_str_mv Phonetic acquisition
Cortical dynamics
Neurobiological plausibility
Neural networks
topic Phonetic acquisition
Cortical dynamics
Neurobiological plausibility
Neural networks
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Many computational theories have been developed to improve artificial phonetic classificationperformance from linguistic auditory streams. However, less attention has been given topsycholinguistic data and neurophysiological features recently found in cortical tissue. Wefocus on a context in which basic linguistic units?such as phonemes?are extracted androbustly classified by humans and other animals from complex acoustic streams in speechdata. We are especially motivated by the fact that 8-month-old human infants can accomplishsegmentation of words from fluent audio streams based exclusively on the statisticalrelationships between neighboring speech sounds without any kind of supervision. In thispaper, we introduce a biologically inspired and fully unsupervised neurocomputationalapproach that incorporates key neurophysiological and anatomical cortical properties,including columnar organization, spontaneous micro-columnar formation, adaptation to contextualactivations and Sparse Distributed Representations (SDRs) produced by means ofpartial N-Methyl-D-aspartic acid (NMDA) depolarization. Its feature abstraction capabilitiesshow promising phonetic invariance and generalization attributes. Our model improves theperformance of a Support Vector Machine (SVM) classifier for monosyllabic, disyllabic andtrisyllabic word classification tasks in the presence of environmental disturbances such aswhite noise, reverberation, and pitch and voice variations. Furthermore, our approachemphasizes potential self-organizing cortical principles achieving improvement without anykind of optimization guidance which could minimize hypothetical loss functions by meansof?for example?backpropagation. Thus, our computational model outperforms multiresolutionspectro-temporal auditory feature representations using only the statistical sequentialstructure immerse in the phonotactic rules of the input stream.
Fil: Dematties, Dario Jesus. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina
Fil: Rizzi, Silvio. Argonne National Laboratory; Estados Unidos
Fil: Thiruvathukal, George. Loyola University; Estados Unidos
Fil: Wainselboim, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; Argentina
Fil: Zanutto, Bonifacio Silvano. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina
description Many computational theories have been developed to improve artificial phonetic classificationperformance from linguistic auditory streams. However, less attention has been given topsycholinguistic data and neurophysiological features recently found in cortical tissue. Wefocus on a context in which basic linguistic units?such as phonemes?are extracted androbustly classified by humans and other animals from complex acoustic streams in speechdata. We are especially motivated by the fact that 8-month-old human infants can accomplishsegmentation of words from fluent audio streams based exclusively on the statisticalrelationships between neighboring speech sounds without any kind of supervision. In thispaper, we introduce a biologically inspired and fully unsupervised neurocomputationalapproach that incorporates key neurophysiological and anatomical cortical properties,including columnar organization, spontaneous micro-columnar formation, adaptation to contextualactivations and Sparse Distributed Representations (SDRs) produced by means ofpartial N-Methyl-D-aspartic acid (NMDA) depolarization. Its feature abstraction capabilitiesshow promising phonetic invariance and generalization attributes. Our model improves theperformance of a Support Vector Machine (SVM) classifier for monosyllabic, disyllabic andtrisyllabic word classification tasks in the presence of environmental disturbances such aswhite noise, reverberation, and pitch and voice variations. Furthermore, our approachemphasizes potential self-organizing cortical principles achieving improvement without anykind of optimization guidance which could minimize hypothetical loss functions by meansof?for example?backpropagation. Thus, our computational model outperforms multiresolutionspectro-temporal auditory feature representations using only the statistical sequentialstructure immerse in the phonotactic rules of the input stream.
publishDate 2019
dc.date.none.fl_str_mv 2019-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/124688
Dematties, Dario Jesus; Rizzi, Silvio; Thiruvathukal, George; Wainselboim, Alejandro Javier; Zanutto, Bonifacio Silvano; Phonetic acquisition in cortical dynamics, a computational approach; Public Library of Science; Plos One; 14; 6; 6-2019; 1-28
1932-6203
CONICET Digital
CONICET
url http://hdl.handle.net/11336/124688
identifier_str_mv Dematties, Dario Jesus; Rizzi, Silvio; Thiruvathukal, George; Wainselboim, Alejandro Javier; Zanutto, Bonifacio Silvano; Phonetic acquisition in cortical dynamics, a computational approach; Public Library of Science; Plos One; 14; 6; 6-2019; 1-28
1932-6203
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0217966
info:eu-repo/semantics/altIdentifier/url/https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0217966
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Public Library of Science
publisher.none.fl_str_mv Public Library of Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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