Advancing Hindi Speech and Handwriting Recognition through Deep Learning in Noisy and Diverse Environments

Abstract

Hindi, as the most spoken language in India, requires advanced speech and handwriting recognition systems to enhance human-machine interaction, particularly in noisy and diverse real-world environments. Traditional automatic speech recognition (ASR) techniques like MFCC and PLP struggle with noise and dialect variations. Recent studies demonstrate that Gammatone Frequency Cepstral Coefficients (GFCC) combined with DNN-HMM architectures significantly improve noise-robust Hindi speech recognition. Similarly, handwriting recognition faces challenges due to script complexity, compound characters, and individual writing styles. Hybrid CNN-RNN architectures with spatial transformers and augmented datasets have shown substantial improvements in recognizing handwritten Hindi text. Furthermore, optical character recognition (OCR) and natural language processing (NLP) for Hindi remain underdeveloped, especially when handling diverse scripts and noisy inputs. This research advocates for large-scale, diverse datasets, Transformer-based models, and cross-lingual learning to enhance recognition capabilities. The integration of multimodal systems and advanced deep learning architectures promises to bridge current gaps, making recognition systems more accurate, inclusive, and applicable across educational, social, and digital domains. This paper presents a comprehensive analysis of existing methodologies and proposes future directions to advance Hindi speech and handwriting recognition through deep learning in complex environments.