Enhancing Machine Translation and Speech Recognition for Low-Resource Indian Languages

Abstract

Machine Translation (MT) and Automatic Speech Recognition (ASR) systems have made significant strides globally; however, low-resource Indian languages remain underrepresented in these technological advancements. The complex linguistic landscape of India, characterized by diverse dialects, code-mixing, and limited annotated data, presents unique challenges for building efficient MT and ASR systems. Recent developments in neural machine translation, transformer models, and time-delay neural networks have shown promise in improving translation accuracy and speech recognition, particularly for Hindi and other regional languages. Despite these efforts, there is a need for more comprehensive datasets, especially in specialized domains like legal and medical terminology, to improve translation fidelity. Moreover, pooling strategies and adaptive modeling in ASR systems must be refined to handle noisy environments and dialect variations effectively. Future work should focus on creating rich, multilingual corpora, advancing transfer learning techniques, and fostering interdisciplinary collaboration. Such approaches will help bridge the digital language divide, enabling more inclusive language technologies for India's linguistically diverse population.