Doctoral Dissertation Research: Documentation and analysis of naturalistic speech to investigate language change patterns due to language contact

This project addresses the documentation and infrastructure developing for a low-resource language. It leverages traditional linguistic fieldwork, digital and computational tools for language documentation and linguistic analysis to document and analyze naturalistic speech data of monolingual and bilingual speakers. Low-resource languages have limited or no digital resources, and they often have few speakers. However, these languages offer valuable insights into language sciences and linguistics, as they serve as robust testing ground for formal linguistic hypotheses, which have traditionally been built on data from major, well-known languages. This research will focus on the documentation of an under-studied language. It will investigate structures found within the speech of monolingual speakers and compare it with that of bilingual speakers. By leveraging digital tools for linguistic annotations, the project will produce an annotated speech corpus as well as language datasets with previously undocumented language structures, such as specialized verb forms and noun markers, which offer valuable insights into language change and language variability. The machine readable infrastructure (database and datasets) that results from this project has the potential to provide new training data for AI systems, which rely on naturalistic language data from a variety of languages. The project advances models in language contact and grammatical restructuring and contributes to debates about language origins. Additionally, it promotes translational research by bridging academic study with real-world impact through language education. All materials will be archived for open access and future use in linguistic and cultural research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.