The Role of Machine Learning in Analyzing Cultural Artifacts

Abstract

This work focuses on artificial intelligence (AI) use in the maintenance of the endangered languages via the combination of computational linguistics, machine learning, and community-based online platforms.  The results show that AI-based tools, such as natural language processing models, automated speech recognizers, neural machine translation systems, and other models, significantly improve the process of documenting, analyzing, and revitalizing minority languages.  Statistical results showed that speech-to-text systems once trained on small, carefully chosen sets achieved accuracy rates in the 85 percent range to enable transcription and preservation of oral traditions with negligible error.  In addition, text generation models aided in the development of multi-linguistic educational contents that facilitated easy acquisition of a language by the people who belonged to various generations.  It increased the number of people involved by enhancing community-based digital archives with the support of AI algorithms. The participation rates increased over 40 percent as compared to the manual methods.  The paper also established that the hybrid methods that utilized both the unsupervised and supervised learning models performed better as compared to single-model pipelines in maintaining the semantic integrity and linguistic diversity.  These findings indicate that AI is not only a technological aid, but a partner in preserving cultural identity by supplying scalable, flexible and context-sensitive language revitalization solutions.  The study points to the remaking power of AI in preventing linguistic extinction and promoting cultural sustainability through blending contemporary computational methods, with sociolinguistic efforts.