Artificial Intelligence Approaches for Preservation of Endangered Languages: A Survey

  • Unique Paper ID: 198053
  • Volume: 12
  • Issue: 11
  • PageNo: 15332-15338
  • Abstract:
  • Languages are essential to human understanding and cultural heritage. However, due to migration, globalization, and a lack of internet presence, many languages are in danger of being extinct. Because they lack adequate linguistic resources, databases, and technological assistance, these languages are frequently referred to as low-resource or endangered languages. New opportunities for recording, digitizing, and renewing such languages have been made possible by recent advancements in artificial intelligence and natural language processing. By transforming speech and text into digital formats and enabling automated analysis, artificial intelligence (AI) technologies including speech recognition, optical character recognition, machine translation, and language modelling can help preserve linguistic knowledge. The contribution of artificial intelligence to the preservation of endangered languages is reviewed in this survey work. It talks about many AI methods, resources, and programs that help in language revitalization and documentation. The study also offers possible research avenues for enhancing AI-based language preservation solutions and emphasizes the difficulties posed by low-resource languages. An example of a regional language that can profit from these technologies is the Tulu language, which is briefly discussed.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{198053,
        author = {Sandhya Bangera and Dr. Subrahmanya Bhat},
        title = {Artificial Intelligence Approaches for Preservation of Endangered Languages: A Survey},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15332-15338},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198053},
        abstract = {Languages are essential to human understanding and cultural heritage. However, due to migration, globalization, and a lack of internet presence, many languages are in danger of being extinct. Because they lack adequate linguistic resources, databases, and technological assistance, these languages are frequently referred to as low-resource or endangered languages. New opportunities for recording, digitizing, and renewing such languages have been made possible by recent advancements in artificial intelligence and natural language processing. By transforming speech and text into digital formats and enabling automated analysis, artificial intelligence (AI) technologies including speech recognition, optical character recognition, machine translation, and language modelling can help preserve linguistic knowledge. The contribution of artificial intelligence to the preservation of endangered languages is reviewed in this survey work. It talks about many AI methods, resources, and programs that help in language revitalization and documentation. The study also offers possible research avenues for enhancing AI-based language preservation solutions and emphasizes the difficulties posed by low-resource languages. An example of a regional language that can profit from these technologies is the Tulu language, which is briefly discussed.},
        keywords = {Artificial Intelligence, Natural Language Processing, Endangered Languages, Low-Resource Languages, Optical Character Recognition, Machine Translation, Language Preservation.},
        month = {April},
        }

Cite This Article

Bangera, S., & Bhat, D. S. (2026). Artificial Intelligence Approaches for Preservation of Endangered Languages: A Survey. International Journal of Innovative Research in Technology (IJIRT), 12(11), 15332–15338.

Related Articles