Exploring Neural Network Models for Pronunciation Improvement in English Language Teaching: A Pedagogical Perspective

Although good pronunciation is essential for efficient communication when learning the English language, standard teaching approaches sometimes fall short of offering individualized education. This article explores how Convolutional Neural Networks (CNNs) might improve pronunciation by providing learners with personalized feedback. The effectiveness of CNN-based pronunciation enhancement systems is assessed in this study by choosing a suitable CNN architecture and creating pronunciation datasets. In order to give learners individualized feedback on their pronunciation accuracy and fluency, the CNN model is trained to assess speech signals and phonetic properties. The findings of the experiments indicate promise; the CNN-based system outperforms traditional techniques in terms of phoneme accuracy and total fluency. This study demonstrates how CNNs may revolutionize pronunciation training by providing customized and flexible learning opportunities for each student. Python software is used to implement the suggested strategy, demonstrating its scalability and practical usefulness. The experiment findings show that CNN-based systems are better to conventional methods, producing improved overall fluency and high phoneme accuracy ($\mathbf{9 8. 5 \%}$). Recommendations for further study are included in the discussion of the pedagogical implications of incorporating CNN-based instruction on pronunciation into language teaching courses. This research advances the field of technology-assisted language learning and emphasizes the value of using neural network models to improve pronunciation in English language instruction.