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Comparison of the prediction accuracy of machine learning algorithms in crosslinguistic vowel classification

Machine learning algorithms can be used for the prediction of nonnative sound classification based on crosslinguistic acoustic similarity. To date, very few linguistic studies have compared the classification accuracy of different algorithms. This study aims to assess how well machines align with hu...

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Autor principal: Georgiou, Georgios P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511699/
https://www.ncbi.nlm.nih.gov/pubmed/37730823
http://dx.doi.org/10.1038/s41598-023-42818-3
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author Georgiou, Georgios P.
author_facet Georgiou, Georgios P.
author_sort Georgiou, Georgios P.
collection PubMed
description Machine learning algorithms can be used for the prediction of nonnative sound classification based on crosslinguistic acoustic similarity. To date, very few linguistic studies have compared the classification accuracy of different algorithms. This study aims to assess how well machines align with human speech perception by assessing the ability of three machine learning algorithms, namely, linear discriminant analysis (LDA), decision tree (C5.0), and neural network (NNET), to predict the classification of second language (L2) sounds in terms of first language (L1) categories. The models were trained using the first three formants and duration of L1 vowels and fed with the same acoustic features of L2 vowels. To validate their accuracy, adult L2 speakers completed a perceptual classification task. The results indicated that NNET predicted with success the classification of all L2 vowels with the highest proportion in terms of L1 categories, while LDA and C5.0 missed only one vowel each. Furthermore, NNET exhibited superior accuracy in predicting the full range of above chance responses, followed closely by LDA. C5.0 did not meet the anticipated performance levels. The findings can hold significant implications for advancing both the theoretical and practical frameworks of speech acquisition.
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spelling pubmed-105116992023-09-22 Comparison of the prediction accuracy of machine learning algorithms in crosslinguistic vowel classification Georgiou, Georgios P. Sci Rep Article Machine learning algorithms can be used for the prediction of nonnative sound classification based on crosslinguistic acoustic similarity. To date, very few linguistic studies have compared the classification accuracy of different algorithms. This study aims to assess how well machines align with human speech perception by assessing the ability of three machine learning algorithms, namely, linear discriminant analysis (LDA), decision tree (C5.0), and neural network (NNET), to predict the classification of second language (L2) sounds in terms of first language (L1) categories. The models were trained using the first three formants and duration of L1 vowels and fed with the same acoustic features of L2 vowels. To validate their accuracy, adult L2 speakers completed a perceptual classification task. The results indicated that NNET predicted with success the classification of all L2 vowels with the highest proportion in terms of L1 categories, while LDA and C5.0 missed only one vowel each. Furthermore, NNET exhibited superior accuracy in predicting the full range of above chance responses, followed closely by LDA. C5.0 did not meet the anticipated performance levels. The findings can hold significant implications for advancing both the theoretical and practical frameworks of speech acquisition. Nature Publishing Group UK 2023-09-20 /pmc/articles/PMC10511699/ /pubmed/37730823 http://dx.doi.org/10.1038/s41598-023-42818-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Georgiou, Georgios P.
Comparison of the prediction accuracy of machine learning algorithms in crosslinguistic vowel classification
title Comparison of the prediction accuracy of machine learning algorithms in crosslinguistic vowel classification
title_full Comparison of the prediction accuracy of machine learning algorithms in crosslinguistic vowel classification
title_fullStr Comparison of the prediction accuracy of machine learning algorithms in crosslinguistic vowel classification
title_full_unstemmed Comparison of the prediction accuracy of machine learning algorithms in crosslinguistic vowel classification
title_short Comparison of the prediction accuracy of machine learning algorithms in crosslinguistic vowel classification
title_sort comparison of the prediction accuracy of machine learning algorithms in crosslinguistic vowel classification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511699/
https://www.ncbi.nlm.nih.gov/pubmed/37730823
http://dx.doi.org/10.1038/s41598-023-42818-3
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