Using Deep Learning and Swarm Intelligence to Achieve Personalized English-Speaking Education

Abstract

This paper presents a pioneering approach to personalized English oral education through the integration of deep learning and swarm intelligence algorithms. Leveraging deep learning techniques, our system offers precise evaluation of various aspects of spoken language, including pronunciation, fluency, and grammatical accuracy. Furthermore, we combine swarm intelligence algorithms to optimize model parameters to achieve optimal performance. We compare the proposed optimization algorithm based on swarm intelligence and its corresponding original algorithm for training comparison to test the effect of the proposed optimizer. Experimental results show that in most cases, the accuracy of the test set using the optimization algorithm based on the swarm intelligence algorithm is better than the corresponding original version, and the training results are more stable. Our experimental results demonstrate the efficacy of the proposed approach in enhancing personalized English oral education, paving the way for transformative advancements in language learning technologies.