The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kajian ini memfokuskan kepada teknik penyesuaian pembesar suara untuk Pembelajaran Bahasa Berbantukan Komputer (CALL). Kami mula-mula menyiasat kesan dan masalah penyesuaian pembesar suara Regresi Linear Kemungkinan Maksimum (MLLR) apabila digunakan dalam penilaian sebutan. Pemarkahan automatik dan eksperimen pengesanan ralat dijalankan pada dua pangkalan data sebutan bahasa Inggeris pelajar Jepun yang tersedia secara terbuka. Seperti yang kami jangkakan, penyesuaian yang berlebihan menyebabkan salah menilai ketepatan sebutan. Berikutan analisis, kami mencadangkan kaedah baru, penyesuaian Regularized Maximum Likelihood Regresi (Regularized-MLLR), untuk menyelesaikan masalah kesan buruk penyesuaian MLLR. Kaedah ini menggunakan sekumpulan data guru untuk menyelaraskan matriks transformasi pelajar supaya sebutan yang salah tidak akan tersilap diubah sebagai yang betul. Kami melaksanakan idea ini dalam dua cara: satu menggunakan purata matriks transformasi guru sebagai kekangan kepada MLLR, dan satu lagi menggunakan gabungan linear matriks guru untuk mewakili transformasi pelajar. Keputusan eksperimen menunjukkan bahawa kaedah yang dicadangkan boleh menggunakan penyesuaian MLLR dengan lebih baik dan mengelakkan penyesuaian berlebihan.
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Salinan
Dean LUO, Yu QIAO, Nobuaki MINEMATSU, Keikichi HIROSE, "Regularized Maximum Likelihood Linear Regression Adaptation for Computer-Assisted Language Learning Systems" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 2, pp. 308-316, February 2011, doi: 10.1587/transinf.E94.D.308.
Abstract: This study focuses on speaker adaptation techniques for Computer-Assisted Language Learning (CALL). We first investigate the effects and problems of Maximum Likelihood Linear Regression (MLLR) speaker adaptation when used in pronunciation evaluation. Automatic scoring and error detection experiments are conducted on two publicly available databases of Japanese learners' English pronunciation. As we expected, over-adaptation causes misjudgment of pronunciation accuracy. Following the analysis, we propose a novel method, Regularized Maximum Likelihood Regression (Regularized-MLLR) adaptation, to solve the problem of the adverse effects of MLLR adaptation. This method uses a group of teachers' data to regularize learners' transformation matrices so that erroneous pronunciations will not be erroneously transformed as correct ones. We implement this idea in two ways: one is using the average of the teachers' transformation matrices as a constraint to MLLR, and the other is using linear combinations of the teachers' matrices to represent learners' transformations. Experimental results show that the proposed methods can better utilize MLLR adaptation and avoid over-adaptation.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.308/_p
Salinan
@ARTICLE{e94-d_2_308,
author={Dean LUO, Yu QIAO, Nobuaki MINEMATSU, Keikichi HIROSE, },
journal={IEICE TRANSACTIONS on Information},
title={Regularized Maximum Likelihood Linear Regression Adaptation for Computer-Assisted Language Learning Systems},
year={2011},
volume={E94-D},
number={2},
pages={308-316},
abstract={This study focuses on speaker adaptation techniques for Computer-Assisted Language Learning (CALL). We first investigate the effects and problems of Maximum Likelihood Linear Regression (MLLR) speaker adaptation when used in pronunciation evaluation. Automatic scoring and error detection experiments are conducted on two publicly available databases of Japanese learners' English pronunciation. As we expected, over-adaptation causes misjudgment of pronunciation accuracy. Following the analysis, we propose a novel method, Regularized Maximum Likelihood Regression (Regularized-MLLR) adaptation, to solve the problem of the adverse effects of MLLR adaptation. This method uses a group of teachers' data to regularize learners' transformation matrices so that erroneous pronunciations will not be erroneously transformed as correct ones. We implement this idea in two ways: one is using the average of the teachers' transformation matrices as a constraint to MLLR, and the other is using linear combinations of the teachers' matrices to represent learners' transformations. Experimental results show that the proposed methods can better utilize MLLR adaptation and avoid over-adaptation.},
keywords={},
doi={10.1587/transinf.E94.D.308},
ISSN={1745-1361},
month={February},}
Salinan
TY - JOUR
TI - Regularized Maximum Likelihood Linear Regression Adaptation for Computer-Assisted Language Learning Systems
T2 - IEICE TRANSACTIONS on Information
SP - 308
EP - 316
AU - Dean LUO
AU - Yu QIAO
AU - Nobuaki MINEMATSU
AU - Keikichi HIROSE
PY - 2011
DO - 10.1587/transinf.E94.D.308
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2011
AB - This study focuses on speaker adaptation techniques for Computer-Assisted Language Learning (CALL). We first investigate the effects and problems of Maximum Likelihood Linear Regression (MLLR) speaker adaptation when used in pronunciation evaluation. Automatic scoring and error detection experiments are conducted on two publicly available databases of Japanese learners' English pronunciation. As we expected, over-adaptation causes misjudgment of pronunciation accuracy. Following the analysis, we propose a novel method, Regularized Maximum Likelihood Regression (Regularized-MLLR) adaptation, to solve the problem of the adverse effects of MLLR adaptation. This method uses a group of teachers' data to regularize learners' transformation matrices so that erroneous pronunciations will not be erroneously transformed as correct ones. We implement this idea in two ways: one is using the average of the teachers' transformation matrices as a constraint to MLLR, and the other is using linear combinations of the teachers' matrices to represent learners' transformations. Experimental results show that the proposed methods can better utilize MLLR adaptation and avoid over-adaptation.
ER -