A Study of Speaking Learning in on Deep Learning Assessment Models

The advent of online education has reshaped the landscape of language instruction, necessitating innovative approaches to assess speaking proficiency in English learners. This study explores the integration of End-to-End Speech Recognition models as a means of enhancing speaking learning assessment in online English education. Leveraging deep learning techniques, the study investigates the accuracy, efficacy, and pedagogical implications of automated assessment mechanisms in transcribing spoken language inputs. A diverse dataset comprising spoken English samples across proficiency levels is utilized to train and evaluate the model's performance. Statistical analysis reveals a high transcription accuracy rate of 92.5%, demonstrating the model's proficiency in capturing nuanced aspects of speaking proficiency such as pronunciation, fluency, and intonation. Comparative analysis against human-based evaluation methods highlights the scalability, consistency, and efficiency advantages offered by automated assessment systems. Despite promising findings, ethical considerations and challenges related to model generalizability warrant further exploration. Overall, this study contributes to advancing speaking learning assessment in online English education and underscores the transformative potential of technology in shaping the future of language instruction practices.