Digital Archives and Preservation Techniques for Revitalising Endangered Languages

Abstract

In the field of linguistic preservation, the integration of digital archives with artificial intelligence (AI)-based tools presents a groundbreaking approach to revitalising endangered languages. Drawing upon the works of Bird et al. (2009) and Krafft and Kusters (2016), this study focuses on the development of a sophisticated digital archive that utilises AI to significantly enhance the preservation and revitalisation of these languages. This research involves creating an extensive digital repository to efficiently store, manage, and provide access to data related to endangered languages, thereby establishing a central hub for housing diverse linguistic materials, including audio, text, and video content. Crucially, this study incorporates AI-driven tools for comprehensive language analysis. These tools, which are essential for accurate language documentation, are adept at tokenizing and analysing texts in endangered languages, ensuring precise processing and preservation, as highlighted by Krauss (1992) and Lewis and Simons (2010). The methodology includes thorough data preparation for language model training, encompassing endangered language-specific tokens, regional dialects, and idiomatic expressions, as noted in research by Clyne (2003) and Bhuvaneswari (2022). The ultimate aim of this study was to preserve and revitalise endangered languages through accessible language learning resources and active community engagement. This study critically evaluates the impact of AI and digital archives on language revitalization by examining their effectiveness in promoting linguistic diversity and cultural preservation, as discussed by Robinson and Yip (2017) and Harrison (2020). This study underscores the potential of AI in enhancing and preserving linguistic and cultural diversity, offering a scalable and sustainable model that makes a significant contribution to the field of linguistic preservation.