Evaluation of aspiration problems in L2 English pronunciation employing machine learning - Publikacja - MOST Wiedzy

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Evaluation of aspiration problems in L2 English pronunciation employing machine learning

Abstrakt

The approach proposed in this study includes methods specifically dedicated to the detection of allophonic variation in English. This study aims to find an efficient method for automatic evaluation of aspiration in the case of Polish second-language (L2) English speakers’ pronunciation when whole words are analyzed instead of particular allophones extracted from words. Sample words including aspirated and unaspirated allophones were prepared by experts in English phonetics and phonology. The datasets created include recordings of words pronounced by nine native English speakers of standard southern British accent and 20 Polish L2 English users. Complete unedited words are treated as input data for feature extraction and classification algorithms such as k-nearest neighbors, naive Bayes method, long-short term memory, and convolutional neural network (CNN). Various signal representations, including low-level audio features, the so-called mid-term and feature trajectory, and spectrograms, are tested in the context of their usability for the detection of aspiration. The results obtained show high potential for an automated evaluation of pronunciation focused on a particular phonological feature (aspiration) when classifiers analyze whole words. Additionally, CNN returns satisfying results for the automated classification of words containing aspirated and unaspirated allophones produced by Polish L2 speakers.

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Wersja publikacji
Accepted albo Published Version
Licencja
Copyright (2021 Acoustical Society of America)

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Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
Journal of the Acoustical Society of America nr 150, strony 120 - 132,
ISSN: 0001-4966
Język:
angielski
Rok wydania:
2021
Opis bibliograficzny:
Piotrowska M., Czyżewski A., Ciszewski T., Korvel G., Kurowski A., Kostek B.: Evaluation of aspiration problems in L2 English pronunciation employing machine learning// Journal of the Acoustical Society of America -Vol. 1, (2021), s.120-132
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1121/10.0005480
Źródła finansowania:
Weryfikacja:
Politechnika Gdańska

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