Research on Intelligent Interaction Design for Enhancing Multi-Scenario English Learning Using ChatGPT and Deep Reinforcement Learning

Abstract

Human-computer interaction scenarios have a broad prospect in the field of English learning. In this paper, a human-computer dialogue interaction system for English learning scenarios is designed based on deep reinforcement learning and artificial intelligence interaction technology. Firstly, a speech enhancement method based on collaborative recurrent network is proposed to optimize the speech analysis module. On this basis, we design the framework of human-computer interaction system, and construct a human-computer dialogue interaction system for English learning scenarios that contains three modules: natural language understanding (NLU), knowledge retrieval enhancement, and natural language generation (NLG), in which knowledge retrieval enhancement utilizes ChatGPT for document reordering design. In the speech enhancement simulation experiments, the mean value of network congestion for the speech enhancement method designed in this paper is 0.073, which achieves at least 50% performance improvement, reduces speech distortion and optimizes the signal-to-noise ratio at the same time. The system is experimentally analyzed for two tasks, conversation state tracking and conversation reply generation, and outperforms the baseline model on both tasks. Finally, a subjective evaluation is conducted, and the system in this paper scores 3.766, which is obviously a smoother human-computer interaction experience, and the English learning interaction experience has a greater advantage compared with the other methods. This paper provides innovative ideas and feasible methods for combining cutting-edge information technology with interactive English teaching.