Utilizing reinforcement learning and support vector machine to enhance feedback mechanism in English speech training

Abstract

This study proposes an innovative approach to enhance the feedback mechanism in English speech training by combining reinforcement learning and support vector machine (SVM) technologies. Traditional feedback mechanisms for English speech training face challenges such as feedback delay, subjectivity, insufficient personalization, technical limitations, and lack of long-term analysis. To address these issues, the SVM model is used to accurately recognize English phonemes or words, enabling real-time evaluation of students’ pronunciation. The reinforcement learning mechanism leverages these real-time evaluations to make personalized adjustments, offering immediate feedback and dynamic training plans. The integration of these two technologies resulted in a multi-dimensional feedback system for assessing pronunciation performance and providing continuous improvement suggestions through long-term data analysis. Experimental results show that the reinforcement learning system using a Q-learning model maintained a high reward mean, significantly improving training effectiveness. Additionally, the SVM model achieved a pronunciation classification accuracy of over 86.54%, with an F1-score of 94.65%. In an 8-week experiment, the experimental group (using the combined Q-learning and SVM system) outperformed the control group (using traditional methods) in pronunciation scores. The experimental group’s score range expanded to 85–94, with a median increase to 89, while the control group’s range was 78–87, with a median increase to 82. The learning progress in the experimental group was significantly higher, demonstrating the effectiveness and superiority of the proposed method.