Accent-Enhanced Automatic Speech Recognition for Multilingual Classrooms
M Jagadeeshvaran, H Sarah Michelle, S Shruti Bala, M Srinivasa Vinayak, T. Deepika · 2024
In an increasingly interconnected world, universities face the challenge of hosting diverse students from different backgrounds. The biggest obstacle to effective communication and learning in multicultural classrooms is teacher and student diversity. To overcome this challenge, we propose the development of noise-enhanced automatic speech recognition (ASR), specifically for multilingual classrooms. The system is designed to provide instant feedback on lessons while preserving the subtleties of speech, thus improving understanding and creating a learning environment. Using the advanced capabilities of the Transformer model together with the convolutional neural network (CNN) highlight classifier, the proposed system provides powerful and accurate transcription capabilities. This article provides an overview of the design, implementation, and evaluation of voice-enhanced ASR, demonstrating its potential to transform communication and learning in a variety of educational settings.