Evaluation of aspiration problems in L2 English pronunciation employing machine learning

Magdalena Piotrowska; Andrzej Czyżewski; Tomasz Ciszewski; Grazina Korvel; Adam Kurowski; Bożena Kostek

doi:10.1121/10.0005480

Evaluation of aspiration problems in L2 English pronunciation employing machine learning

Abstract

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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Authors (6)

Magdalena Piotrowska dr inż.
- Composition Department, Faculty of Composition, Interpretation and Musical Education, Academy of Music, Krakow, Poland
Andrzej Czyżewski prof. dr hab. inż.
Tomasz Ciszewski
- Institute of English and American Studies, Faculty of Languages, University of Gdansk, Gdansk, Poland
Grazina Korvel
- Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania
Adam Kurowski dr inż.
Bożena Kostek prof. dr hab. inż.

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Publication version: Accepted or Published Version
License: Copyright (2021 Acoustical Society of America)

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Articles

Type:

artykuły w czasopismach

Published in:

Journal of the Acoustical Society of America no. 150, pages 120 - 132,
ISSN: 0001-4966

Language:

English

Publication year:

2021

Bibliographic description:

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

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