AI Applications for Endangered Language Preservation

Abstract

This paper explores how artificial intelligence (AI) can be applied to protect endangered languages through computational linguistics and machine learning and natural language processing (NLP).  The findings indicate that AI-based solutions, such speech recognition, text-to-speech synthesis, and neural machine translation, can make documenting and reviving endangered languages faster.  The research indicates that speech-to-text transcription and cross-lingual translation can be more precise by applying deep learning models on phonetic and grammatical structure. This eases the use of the same by the researchers and the native speakers.  Moreover, community-based AI systems were shown to support communal language learning and digital preservation, therefore, helping to maintain intergenerational knowledge.  The findings revealed that the hybrid approaches that combine the use of both supervised and unsupervised learning were superior in detecting dialectal variations. They also demonstrated that reinforcements learning techniques enhanced conversational AI of languages that lack sufficient documentation.  Incorporating AI into study tools provided students with even more reasons to study as well as to preserve their cultural identity.  The research concludes that AI is not solely a technological answer but also a sustainable cultural system, which links the digital innovation to the history of language.  These outcomes demonstrate how AI can transform the world assisting in preserving language diversity and cultural identity.