Design of Oral English Training System Based on Human-Computer Interaction

As an international common language, English is used by a large number of people and can promote international exchanges and cooperation. Learning English is an upsurge, they hope to improve the oral English to achieve the communication between countries, promote economic, cultural and other aspects of the development. The method of learning English in China for a long time is only suitable for exam-oriented education, which is limited by time and space and the environment, and can not meet the needs of the majority of learners. Therefore, this paper intends to design a oral training system, which can flexibly apply human-computer interaction to provide learners with better training methods. This paper aims to explore the design and implementation of an oral English training system based on human-computer interaction. This paper mainly uses the experimental method, comparison method and investigation method, firstly analyzes the demand of oral English training system, then carries on the performance test of the designed system, compares the ability of three intelligent algorithms, and analyzes the performance of human-computer interaction. The experimental results show that the security of long short-term memor (LSTM) network is 0.9, and the security of recurrent neural network (RNN) is second (0.85). deep learning (DL) is the least secure (0.8).