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...
- 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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|
| 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 |
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2023-12-18T15:22:15Z |
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2023-12-18T15:22:15Z |
| dc.date.issued.none.fl_str_mv |
2023-09-05 |
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Documento de conferencia |
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http://purl.org/coar/resource_type/c_c94f |
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http://purl.org/coar/resource_type/c_5794 |
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https://purl.org/redcol/resource_type/EC |
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http://purl.org/coar/version/c_b1a7d7d4d402bcce |
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https://hdl.handle.net/10495/37640 |
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https://hdl.handle.net/10495/37640 |
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eng |
| language |
eng |
| dc.relation.conferencedate.spa.fl_str_mv |
2023-09-04/2023-09-07 |
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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 |
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https://creativecommons.org/licenses/by-nc-sa/4.0/ |
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10 páginas |
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Pilsen, República Checa |
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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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