A systematic approach for English education model based on the neural network algorithm

This paper algorithms based on neural network model designed for English education, to develop a model education system with artificial intelligence, summarized the dimensions were can be used for data analysis related indicators. These indicators include not only the contents of the learning behavi...

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Bibliographic Details
Published inJournal of intelligent & fuzzy systems Vol. 40; no. 2; pp. 3455 - 3466
Main Authors Hui, Wang, Aiyuan, Li
Format Journal Article
LanguageEnglish
Published Amsterdam IOS Press BV 01.01.2021
Subjects
Adaptive systems
Algorithms
Artificial intelligence
Artificial neural networks
Data analysis
Education
Indicators
Learning
Neural networks
Speech recognition
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Summary:This paper algorithms based on neural network model designed for English education, to develop a model education system with artificial intelligence, summarized the dimensions were can be used for data analysis related indicators. These indicators include not only the contents of the learning behavior, test behavior, cooperation behavior and resource search behavior and other human-computer interaction behavior data, also includes demographic background information, learning ability, learning attitude, and other characteristic data that affect the learning effect. We tried to collect relevant indicators to the maximum extent. An audiovisual fusion method based on Convolutional Neural Network (CNN) is proposed. The independent CNN structure is used to realize independent modeling of audiovisual perception and asynchronous information transmission and obtain the description of audiovisual parallel data in the high-dimensional feature space. Following the shared fully connected structure, it is possible to model the long-term dependence of audiovisual parallel data in a higher dimension. Experiments show that the AVSR system built using a CNN-based audiovisual fusion method can achieve a significant performance improvement, and its recognition error rate is relatively reduced by about 15%. The speech recognition system trained with the cross-domain adaptive method can obtain a significant performance improvement, and its recognition error rate is more than 10% lower than that of the baseline system..
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-189383