Is It N avajo? Accurate Language Detection for Endangered Athabaskan Languages (2025)

Abstract: Endangered languages, such as Navajo—the most widely spoken Native American language—are significantly underrepresented in contemporary language technologies, exacerbating the challenges of their preservation and revitalization. This study evaluates Google’s Language Identification (LangID) tool, which does not currently support any Native American languages. To address this, we introduce a random forest classifier trained on Navajo and twenty erroneously suggested languages by LangID. Despite its simplicity, the classifier achieves near-perfect accuracy (97-100%). Additionally, the model demonstrates robustness across other Athabaskan languages—a family of Native American languages spoken primarily in Alaska, the Pacific Northwest, and parts of the Southwestern United States—suggesting its potential for broader application. Our findings underscore the pressing need for NLP systems that prioritize linguistic diversity and adaptability over centralized, one-size-fits-all solutions, especially in supporting underrepresented languages in a multicultural world. This work directly contributes to ongoing efforts to address cultural biases in language models and advocates for the development of culturally localized NLP tools that serve diverse linguistic communities.

## introduction the urgency of preserving endangered languages is not merely a linguistic issue but one deeply connected to the preservation of cultural, historical, and ecological knowledge @xcite @xcite @xcite . these languages reflect the intellectual heritage of diverse communities, playing a critical role in maintaining global cultural diversity. yet, despite this significance, the development of language technologies has been disproportionately focused on languages with large speaker bases and economic clout, leaving languages with smaller populations-such as native american languages-largely unsupported. this study centers on navajo, the most widely spoken native american language @xcite , which remains unsupported by commercial language technologies like google's language identification (langid) tool @xcite . the lack of comprehensive linguistic datasets and dedicated tools impedes both language preservation and learning efforts @xcite . this technological gap is even more pronounced for other native american languages, many of which are on the verge of extinction due to minimal technological integration and educational resources @xcite . google langid's performance on the navajo 10k dataset @xcite revealed complete misidentification of navajo sentences as unrelated languages, an expected outcome given that langid does not currently support any native american language. in response, we developed a language identification model tailored to accurately distinguish navajo from languages erroneously suggested by langid, achieving near-perfect accuracy. this success illustrates that low-resource languages, often overlooked by major technological platforms, can be effectively supported with targeted approaches and resources. beyond navajo, we extended our model to other languages in the athabaskan family-including western apache, mescalero apache, jicarilla apache, and lipan apache @xcite . our model's robustness across these related languages underscores its potential applicability across broader linguistic groups (see figure 1 ). this suggests a viable path for nlp technologies to not only support individual endangered languages but to facilitate revitalization efforts across entire language families. we highlight how centralization in language technology disproportionately benefits global languages, often sidelining underrepresented languages and thus contributing to the erosion of linguistic diversity @xcite . our findings underscore the feasibility of creating decentralized, robust language identification tools, which, by focusing on the unique needs of specific languages, can play a significant role in preserving endangered languages. furthermore, it offers promising pathways for leveraging nlp tools across culturally and linguistically related groups, enriching both academic research and community-driven language revitalization by fostering tools that are responsive to the specific needs of these communities. this aligns with the broader goal of developing nlp technologies that not only accommodate but also actively support the linguistic and cultural diversity of our vibrant multicultural world. ## background while there exist studies on endangered languages @xcite @xcite , their integration into business technologies remains insufficient. for example, although google's langid supports over a hundred languages, it fails to include any native american language, and so provides completely inaccurate suggestions when encountering navajo. similarly, advanced nlp models, such as chatgpt, struggle with navajo due to a lack of training data, which is predominantly derived from more widely spoken languages @xcite . the scarcity of digital resources for navajo further compounds these challenges, as it lacks sufficient digital presence needed for effective nlp tool development @xcite . this scarcity not only limits the use of standard nlp methodologies but also hampers preservation and revitalization efforts. these issues reflect broader market-driven priorities in language technology, which overlook less commercially viable languages (costa-jussà et al., 2022), creating significant barriers to preserving cultural heritage and emphasizing the need for more inclusive technological support for endangered languages @xcite . ## identification for natam languages the benefits of native american language identification are twofold: to bolster the development of linguistic tools tailored to these languages, and to aid in their preservation and revitalization @xcite . effective identification is foundational for creating technologies that understand and process these languages, addressing the significant digital divide in language technology support @xcite . our evaluation aims at a detailed assessment of the models' capability to accurately recognize and differentiate between native american languages and others. by understanding the strengths and limitations of our models, we can refine our techniques to better serve the needs of native american language communities. ## conclusion and future work this study demonstrates the effectiveness of our random forest classifier in accurately distinguishing navajo from languages erroneously suggested by google's langid, as well as effectively recognizing related athabaskan languages. these results emphasize the potential for broader applications in language identification, particularly for underrepresented languages. our findings highlight a significant gap in support for native american languages in current digital platforms, and urge the need for refined, inclusive language models. future work can focus on expanding the classifier's training to include additional native american languages, improving its adaptability, and extending its utility to different language groups. the development of tools capable of distinguishing closely related languages is crucial for supporting educational and communication needs within native communities foot_1 . we also advocate the decentralization of nlp research efforts, emphasizing the need for targeted investment in endangered languages. such initiatives are essential to ensure that advances in language technology promote linguistic equity, thereby preserving cultural diversity and heritage in the digital age.