Construction of English Oral Information Security System Based on Reinforcement Learning Algorithm

Abstract

English oral teaching is one of the important ways to improve English oral ability. Traditional English oral teaching often overlooks the needs of learners for oral learning, adopts a single and unified teaching method, emphasizes theory over practice, and cannot effectively improve learners' oral ability. This article constructs an English oral teaching system based on reinforcement learning, and combines deep learning to construct an oral evaluation module and improve the reward mechanism on the basis of recognizing English oral communication. The experimental results show that the system model proposed in this paper has better oral recognition and evaluation performance, is more in line with practical situations, has higher accuracy, and stronger stability. It can effectively adjust reward situations and stimulate learners' learning motivation based on their status. The application experiment results show that the system in this article can significantly improve the oral performance of learners within the same teaching time, and provide feedback to learners on the existing oral problems from multiple perspectives. Most learners indicate that the system can propose targeted learning strategies, enhance their enthusiasm for oral learning through reward mechanisms, and improve learning efficiency and quality.