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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/124688
Ver los metadatos del registro completo
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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) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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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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1844613908448411648 |
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13.070432 |