Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training.

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build...

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Autores principales:	Yanjing Bi, Chao Li, Yannick Benezeth, Fan Yang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f0039b9e390d42fdb05ed195135de5e2
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spelling	oai:doaj.org-article:f0039b9e390d42fdb05ed195135de5e22021-12-02T20:07:51ZImpacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training.1932-620310.1371/journal.pone.0257901https://doaj.org/article/f0039b9e390d42fdb05ed195135de5e22021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257901https://doaj.org/toc/1932-6203Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 - 8.47% comparing to state-of-the-arts with different data training data density.Yanjing BiChao LiYannick BenezethFan YangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0257901 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yanjing Bi Chao Li Yannick Benezeth Fan Yang Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training.
description	Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 - 8.47% comparing to state-of-the-arts with different data training data density.
format	article
author	Yanjing Bi Chao Li Yannick Benezeth Fan Yang
author_facet	Yanjing Bi Chao Li Yannick Benezeth Fan Yang
author_sort	Yanjing Bi
title	Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training.
title_short	Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training.
title_full	Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training.
title_fullStr	Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training.
title_full_unstemmed	Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training.
title_sort	impacts of multicollinearity on capt modalities: an heterogeneous machine learning framework for computer-assisted french phoneme pronunciation training.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f0039b9e390d42fdb05ed195135de5e2
work_keys_str_mv	AT yanjingbi impactsofmulticollinearityoncaptmodalitiesanheterogeneousmachinelearningframeworkforcomputerassistedfrenchphonemepronunciationtraining AT chaoli impactsofmulticollinearityoncaptmodalitiesanheterogeneousmachinelearningframeworkforcomputerassistedfrenchphonemepronunciationtraining AT yannickbenezeth impactsofmulticollinearityoncaptmodalitiesanheterogeneousmachinelearningframeworkforcomputerassistedfrenchphonemepronunciationtraining AT fanyang impactsofmulticollinearityoncaptmodalitiesanheterogeneousmachinelearningframeworkforcomputerassistedfrenchphonemepronunciationtraining
_version_	1718375276790939648

Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training.

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