Using Speech Recognition Algorithms to Improve Listening Training in College English Listening Instruction

Abstract

English listening proficiency plays a crucial role in language learning and communication. Speech recognition algorithms can significantly enhance listening training in college English instruction by providing personalized and interactive learning experiences. These algorithms can transcribe spoken English passages, allowing students to receive immediate feedback on their comprehension and pronunciation. Additionally, they can generate exercises tailored to individual proficiency levels, offering targeted practice in areas needing improvement. Incorporating speech recognition technology into listening instruction not only promotes active engagement but also enables instructors to track students' progress more effectively. This paper proposed a novel approach leveraging Generative Adversarial Networks (GANs) within an Optimized Edge Computing (OEC) framework to enhance College English Listening Instruction through speech recognition. Traditional methods for English listening instruction often face challenges in providing authentic and personalized learning experiences. To address these limitations, we harness the power of GANs to generate synthetic speech data that closely resembles real-world English speech patterns. By integrating GANs with OEC, we achieve efficient processing of speech data at the edge, minimizing latency and bandwidth consumption while ensuring real-time feedback and interaction. Through extensive experimentation and analysis, demonstrate the superiority of our GAN-OEC framework over traditional baseline models and other speech recognition algorithms. Results demonstrated that the proposed model achieves an average accuracy of 90.6%, a fluency score of 8.5 out of 10, and a pronunciation accuracy of 84.6%. These results highlight the transformative potential of GAN-OEC in revolutionizing English instruction and language learning outcomes in educational settings.