What is it? Towards a Generalizable Native A merican Language Identification System (2025)

Abstract: This paper presents a research thesis proposal to develop a generalizable Native American language identification system. Despite their cultural and historical significance, Native American languages remain entirely unsupported by major commercial language identification systems. This omission not only underscores the systemic neglect of endangered languages in technological development, but also highlights the urgent need for dedicated, community-driven solutions. We propose a two-pronged approach: (1) systematically curating linguistic resources across all Native American languages for robust training, and (2) tailored data augmentation to generate synthetic yet linguistically coherent training samples. As proof of concept, we extend an existing rudimentary Athabaskan language classifier by integrating Plains Apache, an extinct Southern Athabaskan language, as an additional language class. We also adapt a data generation framework for low-resource languages to create synthetic Plains Apache data, highlighting the potential of data augmentation. This proposal advocates for a community-driven, technological approach to supporting Native American languages.

## introduction language is more than a means of communication; it is a vessel of culture, history, and identity @xcite @xcite . for many indigenous communities, the loss of a language represents not just linguistic erosion but the disappearance of traditions, worldviews, and ways of knowing @xcite . despite increasing efforts in computational linguistics to support lowresource languages @xcite , the landscape remains starkly imbalanced. google's langid @xcite , one of the most commercialized language identification systems, covers over 200 languages, but overlooks almost all north american native languages. this exclusion is an alarming reflection of how centralized language technologies systematically marginalize indigenous voices @xcite . the state of new mexico (nm) stands as a crucial focal point in this discussion. home to eight native american languages foot_0 (new mexico secretary of state, 2025), the state exemplifies both the resilience and fragility of indigenous linguistic heritage. while computational linguistics has explored the most-widely spoken navajo to some extent @xcite , progress remains constrained by the scarcity of accessible linguistic data @xcite . to address the current gap in commercialized language technologies, we propose a research agenda to build a generalizable native american language identification system, the first of its kind, as exemplified in figure 1 . our approach consists of two key initiatives: (1) data resource aggregation: a comprehensive, systematic effort to manually collect and curate linguistic datasets across all available native amer-ican languages, ensuring high-quality, representative training data. (2) synthetic data generation: applying an established data augmentation framework for endangered languages to expand existing data, particularly for languages with few or no remaining fluent speakers. for proof of concept, we manually curated a small dataset of 25 plains apache sentences, an extinct southern athabaskan language, and successfully integrated it into an existing rudimentary athabaskan language classifier @xcite . we then adapted a data generation framework for low-resource languages @xcite to create 5 syntactically-coherent new plains apache sentences, displaying the promise of our approach. this paper serves as both a research thesis proposal and a call to action, working towards a future where native american languages are not only included but actively supported by commercialized language technologies. ## related work efforts to develop natural language processing (nlp) technologies for endangered languages are hindered by scarce datasets @xcite and non-specialized model architectures @xcite . this section reviews emergent research in two key areas: native american language classification, and synthetic data generation for endangered languages. @xcite exposed the shortcomings of centralized nlp systems in handling native american languages. google's langid system @xcite , despite covering over 100 languages, failed to include any native american languages, even the most widely spoken navajo @xcite . to address this gap, they developed a random forest classifier (ho, 1995) trained on navajo and 20 of its most frequently confused languages, achieving a near-perfect accuracy (97-100%). further experiments revealed that the classifier generalized well to other athabaskan languages foot_1 under the same family tree, suggesting potential scalability across related language families. however, while this work introduced a novel approach to native american language identification, its scope was limited, covering only five languages. expanding its applicability requires broader generalization across diverse linguistic groups. ## native american language landscape native american languages form a vast and diverse linguistic ecosystem (oberg and olsen-harbich, 2022), reflecting centuries of cultural, historical, and geographical significance @xcite . while many of these languages once flourished across north america, colonization @xcite , forced assimilation policies @xcite , and systemic marginalization @xcite figure 2 : family tree for athabaskan languages have led to widespread language loss. today, their survival depends on urgent and deliberate revitalization efforts (de costa, 2021), including the development of computational tools for language preservation and accessibility. ## language detection experiments to demonstrate the feasibility of our proposed approach mentioned in section 2.3, we conduct a small-scale experiment using plains apache @xcite , an extinct member of the southern athabaskan language family. this proof of concept serves as as a preliminary step in our broader effort to build a generalizable native american language identification system. ## conclusion this paper presents a long-term research vision for developing a generalizable native american language identification system, addressing the critical absence of indigenous languages in commercial language technologies. by building on existing work in native american language classification and synthetic data generation, we propose a unified approach that leverages both to bridge this gap. our small-scale experiments integrating plains apache demonstrate the promise and feasibility of this method. beyond its technical contributions, this work serves as a call to action for the broader nlp community to invest in decentralized, community-driven language technologies that prioritize linguistic diversity. through collective efforts, we can ensure that these languages are not only preserved, but actively recognized and used in the digital age.