Construction of the Open Oral Evaluation Model Based on the Neural Network
According to the problem of low efficiency and low scoring accuracy of the traditional oral language scoring system, this study builds an open oral language evaluation model based on the basic principles of deep learning technology. Firstly, the basic methods of the convolutional neural network (CNN...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2021-01-01
|
Series: | Scientific Programming |
Online Access: | http://dx.doi.org/10.1155/2021/3928246 |
id |
doaj-576d40ae3a0044328ce14b41c22ebed4 |
---|---|
record_format |
Article |
spelling |
doaj-576d40ae3a0044328ce14b41c22ebed42021-10-04T01:58:32ZengHindawi LimitedScientific Programming1875-919X2021-01-01202110.1155/2021/3928246Construction of the Open Oral Evaluation Model Based on the Neural NetworkZhixin Chen0Xu Zhang1Zhiyuan Li2Anchu Li3Northeast Electric Power UniversityNortheast Electric Power UniversityNortheast Electric Power UniversityNortheast Electric Power UniversityAccording to the problem of low efficiency and low scoring accuracy of the traditional oral language scoring system, this study builds an open oral language evaluation model based on the basic principles of deep learning technology. Firstly, the basic methods of the convolutional neural network (CNN) and long short-term memory (LSTM) neural network are introduced. Then, we combine the convolutional neural network (CNN) and long short-term memory (LSTM) neural network to design an open oral scoring model based on CNN + LSTM, which divides the oral evaluation model into the speech scoring model and text scoring model and makes a specific implementation of two scoring models, respectively. An experimental environment is then built to preprocess the data, and finally, the model built in this study is trained and simulated. The experimental results show that the CNN + LSTM network evaluation model has a better comprehensive scoring performance, higher scoring efficiency, and higher accuracy and has feasibility and practicability.http://dx.doi.org/10.1155/2021/3928246 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhixin Chen Xu Zhang Zhiyuan Li Anchu Li |
spellingShingle |
Zhixin Chen Xu Zhang Zhiyuan Li Anchu Li Construction of the Open Oral Evaluation Model Based on the Neural Network Scientific Programming |
author_facet |
Zhixin Chen Xu Zhang Zhiyuan Li Anchu Li |
author_sort |
Zhixin Chen |
title |
Construction of the Open Oral Evaluation Model Based on the Neural Network |
title_short |
Construction of the Open Oral Evaluation Model Based on the Neural Network |
title_full |
Construction of the Open Oral Evaluation Model Based on the Neural Network |
title_fullStr |
Construction of the Open Oral Evaluation Model Based on the Neural Network |
title_full_unstemmed |
Construction of the Open Oral Evaluation Model Based on the Neural Network |
title_sort |
construction of the open oral evaluation model based on the neural network |
publisher |
Hindawi Limited |
series |
Scientific Programming |
issn |
1875-919X |
publishDate |
2021-01-01 |
description |
According to the problem of low efficiency and low scoring accuracy of the traditional oral language scoring system, this study builds an open oral language evaluation model based on the basic principles of deep learning technology. Firstly, the basic methods of the convolutional neural network (CNN) and long short-term memory (LSTM) neural network are introduced. Then, we combine the convolutional neural network (CNN) and long short-term memory (LSTM) neural network to design an open oral scoring model based on CNN + LSTM, which divides the oral evaluation model into the speech scoring model and text scoring model and makes a specific implementation of two scoring models, respectively. An experimental environment is then built to preprocess the data, and finally, the model built in this study is trained and simulated. The experimental results show that the CNN + LSTM network evaluation model has a better comprehensive scoring performance, higher scoring efficiency, and higher accuracy and has feasibility and practicability. |
url |
http://dx.doi.org/10.1155/2021/3928246 |
work_keys_str_mv |
AT zhixinchen constructionoftheopenoralevaluationmodelbasedontheneuralnetwork AT xuzhang constructionoftheopenoralevaluationmodelbasedontheneuralnetwork AT zhiyuanli constructionoftheopenoralevaluationmodelbasedontheneuralnetwork AT anchuli constructionoftheopenoralevaluationmodelbasedontheneuralnetwork |
_version_ |
1716844651609063424 |