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Generative AI Powered Learning Companion for Personalised Education and Broader Accessibility
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 40 |
| Year of Publication: 2025 |
| Authors: Pranjal Sharma, R.K. Sharma |
10.5120/ijca2025925713
|
Pranjal Sharma, R.K. Sharma . Generative AI Powered Learning Companion for Personalised Education and Broader Accessibility. International Journal of Computer Applications. 187, 40 ( Sep 2025), 39-42. DOI=10.5120/ijca2025925713
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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