An Accent Marking Algorithm of English Conversion System Based on Morphological Rules
Facing the English conversion system, the existing accent marking algorithms cannot acquire the morphological rules of English, making the accent marking inaccurate, inefficient, and time-consuming. To solve these problems, this paper puts forward an accent marking algorithm of English conversion sy...
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Kassel University Press
2021-01-01
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Series: | International Journal of Emerging Technologies in Learning (iJET) |
Online Access: | https://online-journals.org/index.php/i-jet/article/view/19717 |
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doaj-44362457da2c45dfb737f3e3cc000aa92021-04-02T19:01:04ZengKassel University PressInternational Journal of Emerging Technologies in Learning (iJET)1863-03832021-01-01160123424610.3991/ijet.v16i01.197177101An Accent Marking Algorithm of English Conversion System Based on Morphological RulesYanxia Zhao0Wei Ren1Zheng Li2Zhejiang Business College, Hangzhou, ChinaZhejiang Business College, Hangzhou, ChinaBeijing Union University, Beijing, ChinaFacing the English conversion system, the existing accent marking algorithms cannot acquire the morphological rules of English, making the accent marking inaccurate, inefficient, and time-consuming. To solve these problems, this paper puts forward an accent marking algorithm of English conversion system based on morphological rules. Specifically, the English audios in a self-developed English corpus were classified by the speaker classification software based on hidden Markov model, as well as audio classification technology, producing the morphological rules of English. After that, the English accents were marked by the maximum entropy model in the English conversion system. The proposed method was proved accurate and efficient in accent marking through experiments. The research results provide a good reference for marking the accents in English conversion system.https://online-journals.org/index.php/i-jet/article/view/19717 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yanxia Zhao Wei Ren Zheng Li |
spellingShingle |
Yanxia Zhao Wei Ren Zheng Li An Accent Marking Algorithm of English Conversion System Based on Morphological Rules International Journal of Emerging Technologies in Learning (iJET) |
author_facet |
Yanxia Zhao Wei Ren Zheng Li |
author_sort |
Yanxia Zhao |
title |
An Accent Marking Algorithm of English Conversion System Based on Morphological Rules |
title_short |
An Accent Marking Algorithm of English Conversion System Based on Morphological Rules |
title_full |
An Accent Marking Algorithm of English Conversion System Based on Morphological Rules |
title_fullStr |
An Accent Marking Algorithm of English Conversion System Based on Morphological Rules |
title_full_unstemmed |
An Accent Marking Algorithm of English Conversion System Based on Morphological Rules |
title_sort |
accent marking algorithm of english conversion system based on morphological rules |
publisher |
Kassel University Press |
series |
International Journal of Emerging Technologies in Learning (iJET) |
issn |
1863-0383 |
publishDate |
2021-01-01 |
description |
Facing the English conversion system, the existing accent marking algorithms cannot acquire the morphological rules of English, making the accent marking inaccurate, inefficient, and time-consuming. To solve these problems, this paper puts forward an accent marking algorithm of English conversion system based on morphological rules. Specifically, the English audios in a self-developed English corpus were classified by the speaker classification software based on hidden Markov model, as well as audio classification technology, producing the morphological rules of English. After that, the English accents were marked by the maximum entropy model in the English conversion system. The proposed method was proved accurate and efficient in accent marking through experiments. The research results provide a good reference for marking the accents in English conversion system. |
url |
https://online-journals.org/index.php/i-jet/article/view/19717 |
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