Automatic Pronunciation Assessment Systems for English Students from Argentina

Autores
Vidal, Jazmín; Bonomi, Cyntia; Riera, Pablo Ernesto; Ferrer, Luciana
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
English proficiency plays a critical role in educational and professional opportunities across Latin America. However, limited access to trained teachers and supplementary resources creates barriers for learners from disadvantaged backgrounds, particularly in Argentina. To address pronunciation learning, this work introduces a computer-assisted pronunciation training (CAPT) system tailored to Argentinian English learners. The approach relies on a novel annotated speech database—EpaDB—specifically designed for evaluating pronunciation at the phone level. To overcome data scarcity, the system leverages transfer learning and self-supervised models (e.g., WavLM+) to extract informative features and train lightweight classifiers. Two methods are compared: mispronunciation detection using non-native data, and phone recognition using native speech. Results demonstrate that models trained on non-native data yield significantly lower error costs and improved scoring accuracy, particularly when matched to the target population. This research highlights the importance of localized datasets and evaluation strategies in developing effective and accessible pronunciation tools for low-resource settings.
Fil: Vidal, Jazmín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
Fil: Bonomi, Cyntia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina
Fil: Riera, Pablo Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
Fil: Ferrer, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
Materia
Pronunciation Scaoring
Computer-assisted pronunciation training
Argentina
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:11336/261670
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Automatic Pronunciation Assessment Systems for English Students from ArgentinaVidal, JazmínBonomi, CyntiaRiera, Pablo ErnestoFerrer, LucianaPronunciation ScaoringComputer-assisted pronunciation trainingArgentinahttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2English proficiency plays a critical role in educational and professional opportunities across Latin America. However, limited access to trained teachers and supplementary resources creates barriers for learners from disadvantaged backgrounds, particularly in Argentina. To address pronunciation learning, this work introduces a computer-assisted pronunciation training (CAPT) system tailored to Argentinian English learners. The approach relies on a novel annotated speech database—EpaDB—specifically designed for evaluating pronunciation at the phone level. To overcome data scarcity, the system leverages transfer learning and self-supervised models (e.g., WavLM+) to extract informative features and train lightweight classifiers. Two methods are compared: mispronunciation detection using non-native data, and phone recognition using native speech. Results demonstrate that models trained on non-native data yield significantly lower error costs and improved scoring accuracy, particularly when matched to the target population. This research highlights the importance of localized datasets and evaluation strategies in developing effective and accessible pronunciation tools for low-resource settings.Fil: Vidal, Jazmín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Bonomi, Cyntia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Riera, Pablo Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Ferrer, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaAssociation for Computing Machinery2024-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/261670Vidal, Jazmín; Bonomi, Cyntia; Riera, Pablo Ernesto; Ferrer, Luciana; Automatic Pronunciation Assessment Systems for English Students from Argentina; Association for Computing Machinery; Communications Of The Acm; 67; 8; 8-2024; 63-670001-0782CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://dl.acm.org/doi/10.1145/3653326info:eu-repo/semantics/altIdentifier/doi/10.1145/3653326info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:08:37Zoai:ri.conicet.gov.ar:11336/261670instacron: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:34982025-09-03 10:08:38.053CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Automatic Pronunciation Assessment Systems for English Students from Argentina
title Automatic Pronunciation Assessment Systems for English Students from Argentina
spellingShingle Automatic Pronunciation Assessment Systems for English Students from Argentina
Vidal, Jazmín
Pronunciation Scaoring
Computer-assisted pronunciation training
Argentina
title_short Automatic Pronunciation Assessment Systems for English Students from Argentina
title_full Automatic Pronunciation Assessment Systems for English Students from Argentina
title_fullStr Automatic Pronunciation Assessment Systems for English Students from Argentina
title_full_unstemmed Automatic Pronunciation Assessment Systems for English Students from Argentina
title_sort Automatic Pronunciation Assessment Systems for English Students from Argentina
dc.creator.none.fl_str_mv Vidal, Jazmín
Bonomi, Cyntia
Riera, Pablo Ernesto
Ferrer, Luciana
author Vidal, Jazmín
author_facet Vidal, Jazmín
Bonomi, Cyntia
Riera, Pablo Ernesto
Ferrer, Luciana
author_role author
author2 Bonomi, Cyntia
Riera, Pablo Ernesto
Ferrer, Luciana
author2_role author
author
author
dc.subject.none.fl_str_mv Pronunciation Scaoring
Computer-assisted pronunciation training
Argentina
topic Pronunciation Scaoring
Computer-assisted pronunciation training
Argentina
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv English proficiency plays a critical role in educational and professional opportunities across Latin America. However, limited access to trained teachers and supplementary resources creates barriers for learners from disadvantaged backgrounds, particularly in Argentina. To address pronunciation learning, this work introduces a computer-assisted pronunciation training (CAPT) system tailored to Argentinian English learners. The approach relies on a novel annotated speech database—EpaDB—specifically designed for evaluating pronunciation at the phone level. To overcome data scarcity, the system leverages transfer learning and self-supervised models (e.g., WavLM+) to extract informative features and train lightweight classifiers. Two methods are compared: mispronunciation detection using non-native data, and phone recognition using native speech. Results demonstrate that models trained on non-native data yield significantly lower error costs and improved scoring accuracy, particularly when matched to the target population. This research highlights the importance of localized datasets and evaluation strategies in developing effective and accessible pronunciation tools for low-resource settings.
Fil: Vidal, Jazmín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
Fil: Bonomi, Cyntia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina
Fil: Riera, Pablo Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
Fil: Ferrer, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
description English proficiency plays a critical role in educational and professional opportunities across Latin America. However, limited access to trained teachers and supplementary resources creates barriers for learners from disadvantaged backgrounds, particularly in Argentina. To address pronunciation learning, this work introduces a computer-assisted pronunciation training (CAPT) system tailored to Argentinian English learners. The approach relies on a novel annotated speech database—EpaDB—specifically designed for evaluating pronunciation at the phone level. To overcome data scarcity, the system leverages transfer learning and self-supervised models (e.g., WavLM+) to extract informative features and train lightweight classifiers. Two methods are compared: mispronunciation detection using non-native data, and phone recognition using native speech. Results demonstrate that models trained on non-native data yield significantly lower error costs and improved scoring accuracy, particularly when matched to the target population. This research highlights the importance of localized datasets and evaluation strategies in developing effective and accessible pronunciation tools for low-resource settings.
publishDate 2024
dc.date.none.fl_str_mv 2024-08
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/11336/261670
Vidal, Jazmín; Bonomi, Cyntia; Riera, Pablo Ernesto; Ferrer, Luciana; Automatic Pronunciation Assessment Systems for English Students from Argentina; Association for Computing Machinery; Communications Of The Acm; 67; 8; 8-2024; 63-67
0001-0782
CONICET Digital
CONICET
url http://hdl.handle.net/11336/261670
identifier_str_mv Vidal, Jazmín; Bonomi, Cyntia; Riera, Pablo Ernesto; Ferrer, Luciana; Automatic Pronunciation Assessment Systems for English Students from Argentina; Association for Computing Machinery; Communications Of The Acm; 67; 8; 8-2024; 63-67
0001-0782
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://dl.acm.org/doi/10.1145/3653326
info:eu-repo/semantics/altIdentifier/doi/10.1145/3653326
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 Association for Computing Machinery
publisher.none.fl_str_mv Association for Computing Machinery
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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score 13.13397

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