Construction of the Open Oral Evaluation Model Based on the Neural Network

Abstract

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.

An Optimal Hierarchical Approach for Oral Cancer Diagnosis Using Rough Set Theory and an Amended Version of the Competitive Search Algorithm

“…The results of evaluating the performance of the technique designed showed that artificial intelligence can detect and identify about 74% of the disease with a sensitivity of 99%, an accuracy of 80%, and a reliability of 0.99%. Chen et al [12] designed an oral assessment design by the Neural Network (NN). According to the low precision of the conventional oral language scoring technology, they used deep learning technology to increase performance.…”