Enhancing Hindi Speech and Text Recognition Using Hybrid Deep Learning and Semantic Models - Barkha Sahu (International Journal of Intelligent Systems and Applications in Engineering, 2022)
The growing adoption of digital technologies among Hindi-speaking populations necessitates advanced speech and text recognition systems tailored to the linguistic complexity of Hindi. Existing models often struggle with phonetic ambiguities, rich morphology, and script variations inherent in the language. This study proposes an integrated framework that combines hybrid deep learning models with semantic approaches to enhance the accuracy and robustness of Hindi speech and text recognition. For speech recognition, multiple feature extraction techniques such as Perceptual Linear Prediction (PLP) and Mel Frequency Cepstral Coefficients (MFCC) are integrated with deep neural networks to minimize substitution and confusion errors. In text recognition, semantic models like Word Sense Disambiguation (WSD) using Hindi WordNet and corpus-based semantic similarity measures improve contextual understanding and disambiguation. Comparative evaluations reveal that this hybrid approach significantly enhances performance across speech-to-text conversion, sentiment analysis, and question-answering tasks. The proposed methodology addresses critical gaps in existing NLP solutions for Hindi and lays the foundation for developing more inclusive and intelligent language processing systems. These advancements are vital for deploying AI-powered services in education, governance, and digital communication tailored to Hindi speakers.