Deep Learning-Based Accent Analysis for Pronunciation Improvement in Language Classrooms

Pronunciation plays a vital role in effective language learning, and accent variations often hinder learners from achieving native-like fluency. Deep learning provides promising avenues to analyze and improve pronunciation in classroom environments automatically. Existing pronunciation training methods rely heavily on manual feedback or traditional speech recognition systems, which often lack precision, scalability, and adaptability across diverse accents. To address these limitations, the proposed framework employs a Residual Neural Network (ResNet) for accent analysis, leveraging its ability to capture fine-grained acoustic features and deep contextual representations. The system provides learners with real-time, phoneme-level feedback, highlighting pronunciation deviations and suggesting corrections specific to their accent patterns. The experimental findings demonstrate significant improvement in learners' pronunciation accuracy and accent reduction compared to conventional methods. This framework enhances personalized learning, supports multilingual classrooms, and empowers teachers with AIdriven assistance.

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