\section{Experimental Setup}
\label{sec:methods}

We fine-tune three multilingual ASR models on all 21 languages: MMS
\texttt{(facebook/mms-1b-all)} \citep{mms_paper}, XLS-R
\texttt{(facebook/wav2vec2-xls-r-1b)} \citep{xls_r_paper}, and Whisper
Large-v3 \texttt{(openai/whisper-large-v3)} \citep{whisper_l_v3_paper}.
All three are trained on multilingual data and have comparable parameter
counts ($1$B for MMS and XLS-R, $1.5$B for Whisper), providing a reasonable basis for practitioner-oriented comparison. Of the 21 languages, MMS has prior exposure to Toba Qom and Greek (of which Cypriot Greek is a dialect); XLS-R and Whisper to
Greek only.

Audio is resampled to $16$ kHz mono, and transcripts are normalized with a
language-agnostic cleaning function. Utterances above the
$97.5$th-percentile duration per language are excluded during training to
avoid memory errors.

For MMS and XLS-R, we adopt parameter-efficient fine-tuning via bottleneck
adapters~\citep{peft_paper}, freezing the pre-trained backbone and updating
only the adapter layers and the CTC head (${\sim}0.25\%$ of parameters).
Whisper is fine-tuned end-to-end using \texttt{Seq2SeqTrainer} with the
pre-trained tokenizer intact. MMS and XLS-R are trained for $15$ epochs
(lr $= 1{\times}10^{-3}$, batch size $16$); Whisper for $8$ epochs
(lr $= 1{\times}10^{-5}$, same batch schedule). The best checkpoints are
selected by validation \texttt{WER}.

We note that these models differ in architecture (encoder-decoder vs. self-supervised CTC) and fine-tuning regime (full vs. adapter). Our goal is not to isolate architectural effects but to benchmark a suitably wide variety of practitioner-accessible options under realistic low-resource constraints, thus providing a substrate for future researchers to build upon.

We report \texttt{WER} as the primary metric and \texttt{CER} as secondary, computed using
the Hugging Face \texttt{evaluate} library. For MMS and XLS-R, we
additionally evaluate beam search decoding with a unigram vocabulary
derived from training transcripts using
\texttt{pyctcdecode}~\citep{heafield-2011-kenlm}, and further with
4-gram ARPA language models.
All experiments were run on a single NVIDIA L40/L40S GPUs on the University
of Washington Hyak cluster, requiring $1$--$3$ GPU hours per language.

We provide further details about the experimental setup in Appendix \ref{appendixC}.