Adaptation approaches for pronunciation scoring with sparse training data

In Computer Assisted Language Learning systems, pronunciation scoring consists in providing a score grading the overall pronunciation quality of the speech uttered by a student. In this work, a log-likelihood ratio obtained with respect to two automatic speech recognition (ASR) models was used as sc...

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Autores principales:	Landini, F., Ferrer, L., Franco, H., Karpov A., Mporas I., Potapova R., ASM Solutions Ltd.
Formato:	SER
Materias:	Computer-assisted language learning Log-likelihood ratio MAP adaptation Pronunciation scoring Computer aided instruction E-learning Grading Linguistics Automatic speech recognition Computer assisted language learning Computer assisted language learning systems Log likelihood ratio Pronunciation quality Scoring performance Speech recognition
Acceso en línea:	http://hdl.handle.net/20.500.12110/paper_03029743_v10458LNAI_n_p87_Landini
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	todo:paper_03029743_v10458LNAI_n_p87_Landini
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spelling	todo:paper_03029743_v10458LNAI_n_p87_Landini2023-10-03T15:18:46Z Adaptation approaches for pronunciation scoring with sparse training data Landini, F. Ferrer, L. Franco, H. Karpov A. Mporas I. Potapova R. ASM Solutions Ltd. Computer-assisted language learning Log-likelihood ratio MAP adaptation Pronunciation scoring Computer aided instruction E-learning Grading Linguistics Automatic speech recognition Computer assisted language learning Computer assisted language learning systems Log likelihood ratio MAP adaptation Pronunciation quality Pronunciation scoring Scoring performance Speech recognition In Computer Assisted Language Learning systems, pronunciation scoring consists in providing a score grading the overall pronunciation quality of the speech uttered by a student. In this work, a log-likelihood ratio obtained with respect to two automatic speech recognition (ASR) models was used as score. One model represents native pronunciation while the other one captures non-native pronunciation. Different approaches to obtain each model and different amounts of training data were analyzed. The best results were obtained training an ASR system using a separate large corpus without pronunciation quality annotations and then adapting it to the native and non-native data, sequentially. Nevertheless, when models are trained directly on the native and non-native data, pronunciation scoring performance is similar. This is a surprising result considering that word error rates for these models are significantly worse, indicating that ASR performance is not a good predictor of pronunciation scoring performance on this system. © Springer International Publishing AG 2017. SER info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_03029743_v10458LNAI_n_p87_Landini
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Computer-assisted language learning Log-likelihood ratio MAP adaptation Pronunciation scoring Computer aided instruction E-learning Grading Linguistics Automatic speech recognition Computer assisted language learning Computer assisted language learning systems Log likelihood ratio MAP adaptation Pronunciation quality Pronunciation scoring Scoring performance Speech recognition
spellingShingle	Computer-assisted language learning Log-likelihood ratio MAP adaptation Pronunciation scoring Computer aided instruction E-learning Grading Linguistics Automatic speech recognition Computer assisted language learning Computer assisted language learning systems Log likelihood ratio MAP adaptation Pronunciation quality Pronunciation scoring Scoring performance Speech recognition Landini, F. Ferrer, L. Franco, H. Karpov A. Mporas I. Potapova R. ASM Solutions Ltd. Adaptation approaches for pronunciation scoring with sparse training data
topic_facet	Computer-assisted language learning Log-likelihood ratio MAP adaptation Pronunciation scoring Computer aided instruction E-learning Grading Linguistics Automatic speech recognition Computer assisted language learning Computer assisted language learning systems Log likelihood ratio MAP adaptation Pronunciation quality Pronunciation scoring Scoring performance Speech recognition
description	In Computer Assisted Language Learning systems, pronunciation scoring consists in providing a score grading the overall pronunciation quality of the speech uttered by a student. In this work, a log-likelihood ratio obtained with respect to two automatic speech recognition (ASR) models was used as score. One model represents native pronunciation while the other one captures non-native pronunciation. Different approaches to obtain each model and different amounts of training data were analyzed. The best results were obtained training an ASR system using a separate large corpus without pronunciation quality annotations and then adapting it to the native and non-native data, sequentially. Nevertheless, when models are trained directly on the native and non-native data, pronunciation scoring performance is similar. This is a surprising result considering that word error rates for these models are significantly worse, indicating that ASR performance is not a good predictor of pronunciation scoring performance on this system. © Springer International Publishing AG 2017.
format	SER
author	Landini, F. Ferrer, L. Franco, H. Karpov A. Mporas I. Potapova R. ASM Solutions Ltd.
author_facet	Landini, F. Ferrer, L. Franco, H. Karpov A. Mporas I. Potapova R. ASM Solutions Ltd.
author_sort	Landini, F.
title	Adaptation approaches for pronunciation scoring with sparse training data
title_short	Adaptation approaches for pronunciation scoring with sparse training data
title_full	Adaptation approaches for pronunciation scoring with sparse training data
title_fullStr	Adaptation approaches for pronunciation scoring with sparse training data
title_full_unstemmed	Adaptation approaches for pronunciation scoring with sparse training data
title_sort	adaptation approaches for pronunciation scoring with sparse training data
url	http://hdl.handle.net/20.500.12110/paper_03029743_v10458LNAI_n_p87_Landini
work_keys_str_mv	AT landinif adaptationapproachesforpronunciationscoringwithsparsetrainingdata AT ferrerl adaptationapproachesforpronunciationscoringwithsparsetrainingdata AT francoh adaptationapproachesforpronunciationscoringwithsparsetrainingdata AT karpova adaptationapproachesforpronunciationscoringwithsparsetrainingdata AT mporasi adaptationapproachesforpronunciationscoringwithsparsetrainingdata AT potapovar adaptationapproachesforpronunciationscoringwithsparsetrainingdata AT asmsolutionsltd adaptationapproachesforpronunciationscoringwithsparsetrainingdata
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Adaptation approaches for pronunciation scoring with sparse training data

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