Design and Application of English Oral Online Dialogue System Based on Reinforcement Learning Algorithm

In view of the lack of personalization and interactivity in current oral English learning, a dialogue system based on reinforcement learning is introduced, aiming to improve users’ oral expression ability by dynamically adjusting the learning path and feedback mechanism. The system adopts a deep Q-learning algorithm, optimizes the voice interaction effect through a reward mechanism, and realizes real-time adaptive adjustment of the dialogue. First, a dialogue framework based on reinforcement learning is designed, in which the user’s voice input is converted into text and used as the input of the system. Then, the system uses a deep Q-learning algorithm to conduct feedback learning based on the user’s voice performance and grammatical errors, and adjusts the dialogue strategy and vocabulary recommendation in real time to improve the interactivity and accuracy of learning. Finally, the system trains multiple rounds of dialogues in a simulated environment to continuously optimize the speech recognition and dialogue response strategies. The whole process uses a reward mechanism to adjust the system behavior based on the actual performance of the user to ensure gradual improvement in the learning process. Learners with a medium foundation (intermediate) have a slight improvement in satisfaction scores, reaching 4.3 points, and their oral test improvement is +15 points. Learners with a high foundation (advanced) have a satisfaction score of 4.5 points in the use of the system, and their oral test improvement is +18 points, thanks to the detailed and accurate feedback provided by the system. By introducing a reinforcement learning algorithm, the designed English oral online dialogue system can adjust learning strategies in real time according to user feedback and improve learning effects. Future research will further optimize the reward mechanism and enhance the system’s adaptive ability and intelligent recommendation function to better serve the needs of different learners.