AI Powered Speech Recognition Tools for Enhancing Communication in Special Needs Learner

Abstract

The development of artificial intelligence (AI) has presented new opportunities in increasing accessibility and inclusivity in learning, especially among students with special needs. This work suggests an AI-based speech recognition system that can enhance communication based on speech processing, deep learning algorithms, and adaptive learning. The system includes features like speech acquisition, preprocessing, feature extraction, acoustic and language modeling and multimodal output generation, text, speech syntheses, and visual aids. The model presented is based on the convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based methods that help to improve the performance of speech recognition and contextual interpretation. Experimental analysis with variety of speech data shows that the proposed system has lower Word Error rate (WER), better accuracy and less latency than the traditional and cloud-based systems of speech recognition. Also, feedback by the users suggests improved engagement and effectiveness of communication. The paper highlights how AI-based assistive technologies could be used to create an inclusive learning experience based on communication challenges and promote equal learning opportunities. Despite addressing the issues of data availability, environmental variability, and ethical considerations, the proposed framework provides a scalable and flexible solution to be used in the real-life. The next step in the work will be directed at enhancing the robustness of models, extending multi-lingual capabilities, and the inclusion of the latest contextual and emotional intelligence features.