\section{Ethical Considerations}

Our work uses the Mozilla Common Voice Spontaneous Speech dataset, which is released under a CC0 license and collected under Mozilla's own consent and contributor framework \cite{ardila-etal-2020-common, mozilla_dataset2026}. We did not collect new data, conduct fieldwork, or interact directly with speakers of any of the 21 languages studied. This shapes both what our work can claim and where its ethical risks lie.

First, we are not members of the Scots or Nubi speech communities, and our linguistic analysis in \S\ref{sec:lg} is therefore an external reading constrained by the orthographies and conventions chosen by the dataset's contributors and validators. We have tried to be explicit about this limitation, particularly for Nubi, where our unfamiliarity with the orthography prevented deeper morphosyntactic analysis. Conclusions about either language should be taken as hypotheses to be verified by community linguists rather than settled findings.

Second, ASR systems for low-resource and endangered languages can cause real harm if deployed without community oversight. Transcription errors of the kinds we document — particularly the catastrophic insertion failures observed in Nubi — could distort downstream documentation, language-learning tools, or accessibility applications, and could misrepresent how a language sounds or behaves. We therefore caution against treating our fine-tuned models as deployment-ready artifacts; they are benchmarks, not products. Any downstream use should involve review by speakers and community stakeholders, with attention to the specific failure modes documented in \S\ref{sec:lg}.

Finally, we recognize that benchmarking work like ours can itself shape research priorities for under-resourced languages by privileging those with existing labeled data. We have tried to mitigate this by performing per-language analysis rather than reporting only averages, and by foregrounding linguistic detail for two specific languages, but we acknowledge that the choice of dataset constrains which communities receive research attention.