Deep Learning Backward Recognition Model to Improve Pronunciation Accuracy in the Teaching of Spoken English

Abstract

The advent of speech recognition algorithms has opened new avenues for enhancing pronunciation accuracy in the teaching of spoken English. As English continues to be a global lingua franca, proficiency in spoken communication holds paramount importance for individuals across diverse professional and academic domains. This paper introduces Forward Backward Recognition Deep Learning (FBRDL), a novel approach aimed at leveraging speech recognition algorithms to enhance pronunciation accuracy in the teaching of spoken English. FBRDL incorporates advanced deep learning techniques such as recurrent neural networks (RNNs) and transformers, which excel in modeling sequential data and capturing long-range dependencies. By leveraging these powerful architectures, FBRDL can effectively handle the inherent variability and complexity of speech signals, enabling robust and accurate recognition even in noisy or adverse environments. Moreover, FBRDL is characterized by its adaptability and scalability, making it well-suited for a wide range of applications across industries. Whether in the realm of virtual assistants, automatic transcription, or voice-controlled devices, FBRDL offers a versatile solution capable of meeting the demands of modern speech recognition tasks. FBRDL integrates principles from deep learning with advanced speech recognition techniques to provide learners with real-time feedback and guidance on their pronunciation. By analyzing spoken English inputs and identifying phonetic discrepancies, FBRDL offers targeted interventions tailored to individual learners' needs. FBRDL achieves an average increase in pronunciation accuracy of 20% compared to traditional teaching methods. Moreover, qualitative assessments underscore the effectiveness of FBRDL in facilitating more precise and efficient acquisition of spoken English skills.