Automatic Pronunciation Assessment of Non-native English based on Phonological Analysis

ABSTRACT: The rapid development of speech recognition systems has motivated the community to work on accent classification, considerably improving the performance of these systems. However, only a few works or tools have focused on evaluating and analyzing in depth not only the accent but also the p...

Full description

Autores:
Escobar Grisales, Daniel
Ríos Urrego, Cristian David
Moreno Acevedo, Santiago Andrés
Pérez Toro, Paula Andrea
Noth, Elmar
Orozco Arroyave, Juan Rafael
Tipo de recurso:
http://purl.org/coar/resource_type/c_5794
Fecha de publicación:
2023
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
eng
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/37640
Acceso en línea:
https://hdl.handle.net/10495/37640
Palabra clave:
Habla
Speech
Inglés - Pronunciación
English languaje - pronunciation
Actos del habla
Speeh acts (linguistics)
Inglés
English language
Fonética
Phonetics
Rights
openAccess
License
https://creativecommons.org/licenses/by-nc-sa/4.0/
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oai_identifier_str oai:bibliotecadigital.udea.edu.co:10495/37640
network_acronym_str UDEA2
network_name_str Repositorio UdeA
repository_id_str
dc.title.spa.fl_str_mv Automatic Pronunciation Assessment of Non-native English based on Phonological Analysis
title Automatic Pronunciation Assessment of Non-native English based on Phonological Analysis
spellingShingle Automatic Pronunciation Assessment of Non-native English based on Phonological Analysis
Habla
Speech
Inglés - Pronunciación
English languaje - pronunciation
Actos del habla
Speeh acts (linguistics)
Inglés
English language
Fonética
Phonetics
title_short Automatic Pronunciation Assessment of Non-native English based on Phonological Analysis
title_full Automatic Pronunciation Assessment of Non-native English based on Phonological Analysis
title_fullStr Automatic Pronunciation Assessment of Non-native English based on Phonological Analysis
title_full_unstemmed Automatic Pronunciation Assessment of Non-native English based on Phonological Analysis
title_sort Automatic Pronunciation Assessment of Non-native English based on Phonological Analysis
dc.creator.fl_str_mv Escobar Grisales, Daniel
Ríos Urrego, Cristian David
Moreno Acevedo, Santiago Andrés
Pérez Toro, Paula Andrea
Noth, Elmar
Orozco Arroyave, Juan Rafael
dc.contributor.author.none.fl_str_mv Escobar Grisales, Daniel
Ríos Urrego, Cristian David
Moreno Acevedo, Santiago Andrés
Pérez Toro, Paula Andrea
Noth, Elmar
Orozco Arroyave, Juan Rafael
dc.contributor.conferencename.spa.fl_str_mv Text, Speech, and Dialogue: International Conference, TSD 2023 (26 : del 4 al 7 de septiembre de 2023, Faculty of Applied Sciences, University of West Bohemia, Pilsen, República Checa)
dc.contributor.researchgroup.spa.fl_str_mv Grupo de Investigación en Telecomunicaciones Aplicadas (GITA)
dc.subject.decs.none.fl_str_mv Habla
Speech
topic Habla
Speech
Inglés - Pronunciación
English languaje - pronunciation
Actos del habla
Speeh acts (linguistics)
Inglés
English language
Fonética
Phonetics
dc.subject.lemb.none.fl_str_mv Inglés - Pronunciación
English languaje - pronunciation
Actos del habla
Speeh acts (linguistics)
Inglés
English language
Fonética
Phonetics
description ABSTRACT: The rapid development of speech recognition systems has motivated the community to work on accent classification, considerably improving the performance of these systems. However, only a few works or tools have focused on evaluating and analyzing in depth not only the accent but also the pronunciation level of a person when learning a non-native language. Our study aims to evaluate the pronunciation skills of non-native English speakers whose first language is Arabic, Chinese, Spanish, or French. We considered training a system to compute posterior probabilities of phonological classes from English native speakers and then evaluating whether it is possible to discriminate between native English speakers vs. non-native English speakers. Posteriors of each phonological class separately and also their combination are considered. Phonemes with low posterior results are used to give feedback to the speaker regarding which phonemes should be improved. The results suggest that it is possible to distinguish between each of the non-native languages and native English with accuracies between 67.6% and 80.6%. According to our observations, the most discriminant phonological classes are alveolar, lateral, velar, and front. Finally, the paper introduces a graphical way to interpret the results phoneme-by-phoneme, such that the speaker receives feedback about his/her pronunciation performance.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-12-18T15:22:15Z
dc.date.available.none.fl_str_mv 2023-12-18T15:22:15Z
dc.date.issued.none.fl_str_mv 2023-09-05
dc.type.spa.fl_str_mv Documento de conferencia
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_c94f
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_5794
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dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/10495/37640
url https://hdl.handle.net/10495/37640
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.conferencedate.spa.fl_str_mv 2023-09-04/2023-09-07
dc.relation.conferenceplace.spa.fl_str_mv Faculty of Applied Sciences, University of West Bohemia, Pilsen, República Checa
dc.relation.ispartofjournal.spa.fl_str_mv Text, Speech, and Dialogue: 26th International Conference, TSD 2023
dc.rights.uri.spa.fl_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
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spelling Escobar Grisales, DanielRíos Urrego, Cristian DavidMoreno Acevedo, Santiago AndrésPérez Toro, Paula AndreaNoth, ElmarOrozco Arroyave, Juan RafaelText, Speech, and Dialogue: International Conference, TSD 2023 (26 : del 4 al 7 de septiembre de 2023, Faculty of Applied Sciences, University of West Bohemia, Pilsen, República Checa)Grupo de Investigación en Telecomunicaciones Aplicadas (GITA)2023-12-18T15:22:15Z2023-12-18T15:22:15Z2023-09-05https://hdl.handle.net/10495/37640ABSTRACT: The rapid development of speech recognition systems has motivated the community to work on accent classification, considerably improving the performance of these systems. However, only a few works or tools have focused on evaluating and analyzing in depth not only the accent but also the pronunciation level of a person when learning a non-native language. Our study aims to evaluate the pronunciation skills of non-native English speakers whose first language is Arabic, Chinese, Spanish, or French. We considered training a system to compute posterior probabilities of phonological classes from English native speakers and then evaluating whether it is possible to discriminate between native English speakers vs. non-native English speakers. Posteriors of each phonological class separately and also their combination are considered. Phonemes with low posterior results are used to give feedback to the speaker regarding which phonemes should be improved. The results suggest that it is possible to distinguish between each of the non-native languages and native English with accuracies between 67.6% and 80.6%. According to our observations, the most discriminant phonological classes are alveolar, lateral, velar, and front. Finally, the paper introduces a graphical way to interpret the results phoneme-by-phoneme, such that the speaker receives feedback about his/her pronunciation performance.Universidad de Antioquia. Vicerrectoría de investigación. Comité para el Desarrollo de la Investigación - CODICOL004444810 páginasapplication/pdfenghttps://creativecommons.org/licenses/by-nc-sa/4.0/http://creativecommons.org/licenses/by-nc-sa/2.5/co/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Automatic Pronunciation Assessment of Non-native English based on Phonological AnalysisDocumento de conferenciahttp://purl.org/coar/resource_type/c_5794http://purl.org/coar/resource_type/c_c94fhttps://purl.org/redcol/resource_type/EChttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/draftPilsen, República ChecaHablaSpeechInglés - PronunciaciónEnglish languaje - pronunciationActos del hablaSpeeh acts (linguistics)InglésEnglish languageFonéticaPhonetics2023-09-04/2023-09-07Faculty of Applied Sciences, University of West Bohemia, Pilsen, República ChecaText, Speech, and Dialogue: 26th International Conference, TSD 2023PRG2017-15530 Analysis of architectures based on deep learning methods to evaluate and recognize traits in speech signals.ES92210001PI2023-58010PRG2017-15530RoR:03bp5hc83$99.519.000PublicationORIGINALEscobarDaniel_2023_Pronunciation.pdfEscobarDaniel_2023_Pronunciation.pdfDocumento de conferenciaapplication/pdf624025https://bibliotecadigital.udea.edu.co/bitstreams/3b3b4859-c277-4f21-a9bd-7f5571633d3b/download930ac01ca76b556ac12f1346a41c13a1MD51trueAnonymousREADEscobarDaniel_2023_Pronunciation_Poster.pdfEscobarDaniel_2023_Pronunciation_Poster.pdfPósterapplication/pdf1188868https://bibliotecadigital.udea.edu.co/bitstreams/487f8ab5-b6e1-46ce-904f-f1cde98facd3/download86d8e8d1d5aabf727b605464f868dfa7MD52falseAnonymousREADCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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