Machine Learning–based Analysis of English Lateral Allophones
Automatic classification methods, such as artificial neural networks (ANNs), the k-nearest neighbor (kNN) and self-organizing maps (SOMs), are applied to allophone analysis based on recorded speech. A list of 650 words was created for that purpose, containing positionally and/or contextually conditi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Sciendo
2019-06-01
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| Series: | International Journal of Applied Mathematics and Computer Science |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amcs-2019-0029 |
| _version_ | 1818587142766460928 |
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| author | Piotrowska Magdalena Korvel Gražina Kostek Bożena Ciszewski Tomasz Cżyzewski Andrzej |
| author_facet | Piotrowska Magdalena Korvel Gražina Kostek Bożena Ciszewski Tomasz Cżyzewski Andrzej |
| author_sort | Piotrowska Magdalena |
| collection | DOAJ |
| description | Automatic classification methods, such as artificial neural networks (ANNs), the k-nearest neighbor (kNN) and self-organizing maps (SOMs), are applied to allophone analysis based on recorded speech. A list of 650 words was created for that purpose, containing positionally and/or contextually conditioned allophones. For each word, a group of 16 native and non-native speakers were audio-video recorded, from which seven native speakers’ and phonology experts’ speech was selected for analyses. For the purpose of the present study, a sub-list of 103 words containing the English alveolar lateral phoneme /l/ was compiled. The list includes ‘dark’ (velarized) allophonic realizations (which occur before a consonant or at the end of the word before silence) and 52 ‘clear’ allophonic realizations (which occur before a vowel), as well as voicing variants. The recorded signals were segmented into allophones and parametrized using a set of descriptors, originating from the MPEG 7 standard, plus dedicated time-based parameters as well as modified MFCC features proposed by the authors. Classification methods such as ANNs, the kNN and the SOM were employed to automatically detect the two types of allophones. Various sets of features were tested to achieve the best performance of the automatic methods. In the final experiment, a selected set of features was used for automatic evaluation of the pronunciation of dark /l/ by non-native speakers. |
| first_indexed | 2024-12-16T09:04:10Z |
| format | Article |
| id | doaj.art-dd056168f88c4493970a53c5f3a7ab07 |
| institution | Directory Open Access Journal |
| issn | 2083-8492 |
| language | English |
| last_indexed | 2024-12-16T09:04:10Z |
| publishDate | 2019-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | International Journal of Applied Mathematics and Computer Science |
| spelling | doaj.art-dd056168f88c4493970a53c5f3a7ab072022-12-21T22:37:07ZengSciendoInternational Journal of Applied Mathematics and Computer Science2083-84922019-06-0129239340510.2478/amcs-2019-0029amcs-2019-0029Machine Learning–based Analysis of English Lateral AllophonesPiotrowska Magdalena0Korvel Gražina1Kostek Bożena2Ciszewski Tomasz3Cżyzewski Andrzej4Multimedia Systems Department, Gdansk University of Technology, G. Narutowicza 11/12, 80-233Gdansk, PolandInstitute of Data Science and Digital Technologies, Vilnius University, Akademijos 4, LT-04812Vilnius, LithuaniaLaboratory of Audio Acoustics, Gdansk University of Technology, G. Narutowicza 11/12, 80-233Gdansk, PolandMultimedia Systems Department, Gdansk University of Technology, G. Narutowicza 11/12, 80-233Gdansk, PolandMultimedia Systems Department, Gdansk University of Technology, G. Narutowicza 11/12, 80-233Gdansk, PolandAutomatic classification methods, such as artificial neural networks (ANNs), the k-nearest neighbor (kNN) and self-organizing maps (SOMs), are applied to allophone analysis based on recorded speech. A list of 650 words was created for that purpose, containing positionally and/or contextually conditioned allophones. For each word, a group of 16 native and non-native speakers were audio-video recorded, from which seven native speakers’ and phonology experts’ speech was selected for analyses. For the purpose of the present study, a sub-list of 103 words containing the English alveolar lateral phoneme /l/ was compiled. The list includes ‘dark’ (velarized) allophonic realizations (which occur before a consonant or at the end of the word before silence) and 52 ‘clear’ allophonic realizations (which occur before a vowel), as well as voicing variants. The recorded signals were segmented into allophones and parametrized using a set of descriptors, originating from the MPEG 7 standard, plus dedicated time-based parameters as well as modified MFCC features proposed by the authors. Classification methods such as ANNs, the kNN and the SOM were employed to automatically detect the two types of allophones. Various sets of features were tested to achieve the best performance of the automatic methods. In the final experiment, a selected set of features was used for automatic evaluation of the pronunciation of dark /l/ by non-native speakers.https://doi.org/10.2478/amcs-2019-0029allophonesaudio featuresartificial neural networks (anns)k-nearest neighbor (knn)self-organizing map (som) |
| spellingShingle | Piotrowska Magdalena Korvel Gražina Kostek Bożena Ciszewski Tomasz Cżyzewski Andrzej Machine Learning–based Analysis of English Lateral Allophones International Journal of Applied Mathematics and Computer Science allophones audio features artificial neural networks (anns) k-nearest neighbor (knn) self-organizing map (som) |
| title | Machine Learning–based Analysis of English Lateral Allophones |
| title_full | Machine Learning–based Analysis of English Lateral Allophones |
| title_fullStr | Machine Learning–based Analysis of English Lateral Allophones |
| title_full_unstemmed | Machine Learning–based Analysis of English Lateral Allophones |
| title_short | Machine Learning–based Analysis of English Lateral Allophones |
| title_sort | machine learning based analysis of english lateral allophones |
| topic | allophones audio features artificial neural networks (anns) k-nearest neighbor (knn) self-organizing map (som) |
| url | https://doi.org/10.2478/amcs-2019-0029 |
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