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Implementation of a System for Assessing the Quality of Spoken English Pronunciation Based on Cognitive Heuristic Computing

This paper analyzes and investigates the quality assessment of spoken English pronunciation using a cognitive heuristic computing approach and designs a corresponding spoken pronunciation quality assessment system for practical training. Using the general Goodness of Pronunciation assessment algorit...

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Autores principales: Wu, Yanping, Zheng, Changlong, Hao, Meihui, Wang, Linlin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9286978/
https://www.ncbi.nlm.nih.gov/pubmed/35845915
http://dx.doi.org/10.1155/2022/5239375
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author Wu, Yanping
Zheng, Changlong
Hao, Meihui
Wang, Linlin
author_facet Wu, Yanping
Zheng, Changlong
Hao, Meihui
Wang, Linlin
author_sort Wu, Yanping
collection PubMed
description This paper analyzes and investigates the quality assessment of spoken English pronunciation using a cognitive heuristic computing approach and designs a corresponding spoken pronunciation quality assessment system for practical training. Using the general Goodness of Pronunciation assessment algorithm as a benchmark, the shortcomings of the traditional Goodness of Pronunciation method are explored through statistical experiments, and the validity of the overall posterior probability output from the speech model for pronunciation quality assessment is verified. For the analysis of rhythm, there is no common algorithm framework, but in this paper, the F0 similarity algorithm based on dynamic time regularization and the stop similarity algorithm based on forced alignment is proposed for the two main factors of rhythm, intonation, and pause, respectively. After framing, the Hamming window processing is used to make the signal smoother, reduce the side lobe size after fast Fourier transform processing, and solve the problem of spectrum leakage. Compared with the ordinary rectangular window function, the Hamming window can obtain a higher quality spectrum. And combined with CTC for speech recognition modeling, the recognition rates are comparable in the case of using BLSTM and bidirectional threshold cyclic unit BGRU as the hidden layer unit, respectively, and the training time is 23% less than BLSTM using BGRU; in addition, the BGRU-CTC model is improved by using a 2-BGRU-CTC model with 256 hidden layer nodes, so that the error rate of phoneme recognition is reduced to 33%. The effectiveness of the algorithm framework is also verified through experiments, which further proves the effectiveness of our proposed phoneme segment feature and rhyme similarity algorithm.
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spelling pubmed-92869782022-07-16 Implementation of a System for Assessing the Quality of Spoken English Pronunciation Based on Cognitive Heuristic Computing Wu, Yanping Zheng, Changlong Hao, Meihui Wang, Linlin Comput Intell Neurosci Research Article This paper analyzes and investigates the quality assessment of spoken English pronunciation using a cognitive heuristic computing approach and designs a corresponding spoken pronunciation quality assessment system for practical training. Using the general Goodness of Pronunciation assessment algorithm as a benchmark, the shortcomings of the traditional Goodness of Pronunciation method are explored through statistical experiments, and the validity of the overall posterior probability output from the speech model for pronunciation quality assessment is verified. For the analysis of rhythm, there is no common algorithm framework, but in this paper, the F0 similarity algorithm based on dynamic time regularization and the stop similarity algorithm based on forced alignment is proposed for the two main factors of rhythm, intonation, and pause, respectively. After framing, the Hamming window processing is used to make the signal smoother, reduce the side lobe size after fast Fourier transform processing, and solve the problem of spectrum leakage. Compared with the ordinary rectangular window function, the Hamming window can obtain a higher quality spectrum. And combined with CTC for speech recognition modeling, the recognition rates are comparable in the case of using BLSTM and bidirectional threshold cyclic unit BGRU as the hidden layer unit, respectively, and the training time is 23% less than BLSTM using BGRU; in addition, the BGRU-CTC model is improved by using a 2-BGRU-CTC model with 256 hidden layer nodes, so that the error rate of phoneme recognition is reduced to 33%. The effectiveness of the algorithm framework is also verified through experiments, which further proves the effectiveness of our proposed phoneme segment feature and rhyme similarity algorithm. Hindawi 2022-07-08 /pmc/articles/PMC9286978/ /pubmed/35845915 http://dx.doi.org/10.1155/2022/5239375 Text en Copyright © 2022 Yanping Wu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wu, Yanping
Zheng, Changlong
Hao, Meihui
Wang, Linlin
Implementation of a System for Assessing the Quality of Spoken English Pronunciation Based on Cognitive Heuristic Computing
title Implementation of a System for Assessing the Quality of Spoken English Pronunciation Based on Cognitive Heuristic Computing
title_full Implementation of a System for Assessing the Quality of Spoken English Pronunciation Based on Cognitive Heuristic Computing
title_fullStr Implementation of a System for Assessing the Quality of Spoken English Pronunciation Based on Cognitive Heuristic Computing
title_full_unstemmed Implementation of a System for Assessing the Quality of Spoken English Pronunciation Based on Cognitive Heuristic Computing
title_short Implementation of a System for Assessing the Quality of Spoken English Pronunciation Based on Cognitive Heuristic Computing
title_sort implementation of a system for assessing the quality of spoken english pronunciation based on cognitive heuristic computing
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9286978/
https://www.ncbi.nlm.nih.gov/pubmed/35845915
http://dx.doi.org/10.1155/2022/5239375
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