\section{Related Works}
\label{sec:related}
\subsection{Automatic Speech Recognition}
ASR for low-resource and endangered languages is a well-established research topic with a long history, resulting in a vast body of prior literature \citep{asr_under_rsrc_survey}. Methodologically, the field has progressed from early acoustic modeling using graphemes, Hidden Markov Models (HMMs), and Dynamic Time Warping to modern Connectionist Temporal Classification (CTC) and wav2vec-based speech Large Language Models \citep{asr_vietnamese, nmt_low_rsrc, mms_paper, xls_r_paper, whisper_l_v3_paper}. Despite these advancements, the majority of the 7000+ languages in the world are still low-resource, with many of them being endangered. Organizing fieldwork to obtain realistic recordings from native speakers and gold labels from capable translators is an extremely complex task. For instance, \citet{mixtec_ipa} perform extensive fieldwork for San Martín Peras Mixtec, which is a variety of Mixtec (in this paper, we analyse \texttt{WER} on Southwestern Tlaxiaco Mixtec, which is distant from this dialect but has substantial overlap).

Recent research at the University of Washington has leveraged large multilingual models to mitigate these issues. \citet{liang-levow-2025-breaking} benchmarked \texttt{MMS} and \texttt{XLS-R} on Cicipu, Mocho’, Toratán, Ulwa, and Upper Napo Kichwa, finding that fine-tuned multilingual ASR models can substantially reduce the transcription burden for low-resource languages. Additionally, \citep{mainzinger-levow-2024-fine} investigated ASR for the American indigenous language Mvskoke, and their analysis supported the above findings.

Future work on expanding the scope of speech technologies to endangered languages should be carried out in tandem with HCI researchers \citep{asr_hci}, local communities, and both direct and indirect stakeholders \citep{imam-etal-2025-automatic, alabi-etal-2025-charting}. 

\subsection{Resources for Scots}
The Scots language represents a linguistic continuum between Scottish Standard English and ``Broad Scots'', featuring high regional variation \citep{douglas_scot_problems}. Despite having over 1.5 million speakers, it remains low-resource in natural language processing (NLP), with a critical scarcity of annotated audio data compared to higher-resource languages \citep{blaschke-etal-2023-survey}.

\subsubsection{Speech Corpora and Accessibility}
The primary resource for the language is the Scottish Corpus of Texts \& Speech (SCOTS), which offers over 800,000 words of oral history, interviews, and casual conversation \citep{Anderson2007}. This dataset is publicly available and free of charge, providing synchronized orthographic transcriptions and metadata.

Other significant, publicly accessible Scots speech resources include:

\begin{itemize}
    \item The Scots Syntax Atlas (SCOSYA): Comprises 275 hours of conversational audio from 530 speakers across 146 locations, accompanied by acceptability judgments \citep{scots_syntax_atlas, adger2023intuitions}.
    \item Google’s Multi-speaker British Isles Accents: An open-source dataset containing high-quality audio with roughly 10 hours specifically dedicated to Scottish accents \citep{demirsahin-etal-2020-open}.
    \item Freiburg English Dialect Corpus (FRED): Includes approximately 300 hours of speech from the UK, with specific subsets for the Hebrides and Scottish Highlands \citep{Anderwald2007}.
    \item Mozilla Common Voice: Provides spontaneous speech data for Scots, used in recent low-resource ASR challenges \citep{ardila-etal-2020-common}.
\end{itemize}

While the total volume of documented Scots audio exceeds 600 hours, the percentage of gold-standard labeled data suitable for training neural models is much smaller. Most corpora are labeled with orthographic transcriptions, though some, like the Google accent dataset, provide high phoneme coverage for phonetic analysis. Works such as \citet{MoChridhe_2020} examine SCOTS and the Wee Windaes\footnote{\url{https://wee-windaes.nls.uk}} project within the framework of digital humanities and critical engagement.

\subsubsection{NLP Research on Scots Speech/Text}
NLP work on Scots speech was historically scarce, but recent efforts have shifted toward ASR using transformer-based architectures. \citet{xls_r_paper} utilized mere 2 hours of Scots data to pre-train the XLS-R model, highlighting the extreme data constraints researchers face. \citet{rafkin2026taskarithmeticsupportlanguages} explored task arithmetic by fine-tuning Whisper-tiny and Whisper-large-v3. They leveraged the genetic relationship between Scots and English to improve performance on spontaneous speech sets.
\citet{lameris-stymne-2021-whits} developed Part-of-Speech (POS) tagging models specifically for Scots using annotated sentences derived from the SCOTS corpus.They manually tagged a small set of data, examined zero-shot and transfer learning methods to English, and fine-tuned a model to determine parts of speech. \citet{scots_spade} built the Integrated Speech Corpus Analysis (ISCAN) system to perform large-scale automated acoustic analysis across Scots corpora, such as SoTC and SCOTS (The SPADE Project).

\subsection{Resources for Nubi}

Speech modeling for Nubi faces the typical hurdles of low-resource and endangered languages. Nubi currently lacks a robust digital presence, with existing written materials often unstandardized and insufficient for complex model training. Recent efforts have shifted toward systematic documentation; notably, \citet{otieno2024framework} developed a framework to build linguistic corpora for Kisii Town Heritage Nubian, which involves collecting recorded audio, video, and manual transcripts. Additionally, the Spontaneous Speech Dataset by Mozilla Common Voice serves as an important, albeit low-resource, external resource for naturalistic audio \citep{ardila-etal-2020-common}.

To the best of our knowledge, there is currently no documented prior work in the sources regarding speech or text modeling for the Nubi language. 

% This underlines the importance of linguistic fieldwork in collecting more high quality audio data, collaborating with native speakers and experts in obtaining labels, and building speech technologies to aid language revitalization and preservation.

% Work done at Aarhus University in Denmark claims that creole languages such as Nubi as "structurally less complex than other natural languages" (cite later: https://aclanthology.org/W12-0213.pdf) through the use of phylogenetic networks. 

