Abstract

This research presents the development and evaluation of a hybrid Convolutional Neural Network (CNN) and the Bidirectional long -term short -term memory (BILSTM) model for speech recognition, especially tailored for educational applications. Using the Mozilla Common Voice Dataset, the model suffered an impressive testing accuracy of 91.87% and less testing loss of 0.2966. The study highlighted the importance of effective preprocessing, including noise reduction, audio trimming, and MEL-Frequency Cepstral Coefficients (MFCC) feature extraction, which were necessary to improve model performance. The CNN-BiLSTM architecture enabled the model to capture both local and long-range temporary dependence, making it strong for diverse accents, speech speeds and background noise. This task reflects the viability of implementing advanced speech recognition systems in the generative AI-in-charge learners, contributing to the manufacture of inclusive and accessible educational devices. Future research can detect fine-tuning for specific domains to carry forward multilingual dataset, attention mechanisms, and performance.

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