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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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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. |
format | Online Article Text |
id | pubmed-9286978 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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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