A practical study of English pronunciation correction using deep learning techniques

Abstract

In the context of globalization, the accuracy of English pronunciation has become increasingly important for non-native speakers. This study aims to explore the application and effectiveness of deep learning techniques in English pronunciation correction. Through a series of experimental studies, we compared the effectiveness of deep learning techniques with traditional pronunciation teaching methods, focusing on measures such as pronunciation accuracy, fluency, and learner satisfaction. The study found that deep learning techniques were significantly better than traditional methods in improving pronunciation accuracy and fluency, and achieved higher learner satisfaction. In the face of challenges such as data quality and model generalization, the research has effectively addressed these challenges through data enhancement and optimization of model structure. These results show that deep learning technology has great potential and application prospects in the field of language learning. Future research could integrate broader speech data and explore more complex model architectures to further improve model performance and adaptability.