Hybrid Transliteration Framework for Kashmiri : Managing Diacritic Subjection in English-to-Native Script Conversion

Abstract

In a world where connections between people are growing, effective communication between languages is essential. Machine translation systems play a pivotal role in facilitating seamless text conversion between languages, enabling communication in diverse fields such as entertainment, education, and commerce. However, certain linguistic elements, such as named entities, require specialized treatment to preserve their phonetic integrity across languages. Transliteration, the process of converting words while maintaining pronunciation and phonetics, addresses this need. This research focuses on developing a transliteration system from English to Kashmiri, recognizing the importance of maintaining phonological characteristics during script conversion. Leveraging natural language processing techniques, the system aims to efficiently convert text while ensuring readability for individuals from diverse linguistic backgrounds. The approach combines phoneme-based and grapheme-based transliteration methods with diacritic subjection to accurately represent sounds and characters in the target script. By bridging linguistic barriers and enhancing cross-linguistic communication, this research contributes to the accessibility and inclusivity of information across different language communities.