\section{Dataset Overview}
\label{sec:data}
\subsection{Shared Task Information}
For this study, we utilized data from the Mozilla Common Voice Spontaneous Speech ASR Shared Task. This shared task is based on the recently released spontaneous speech datasets from Mozilla Common Voice. In these datasets, participants freely respond to prompts, and the responses are transcribed and validated, providing a diverse set of examples suitable for training and evaluating our models \cite{mozilla_dataset2026}. For all 21 languages in the dataset, we list the countries of origin, vitality, and number of speakers in Appendix \ref{appendixA}. Hours spent on training, development, and test sets for each language are also included in Appendix \ref{appendixA}. In Appendix \ref{appendixB}, we list complete statistics of utterance durations for all languages. Of particular importance is the number \textbf{P97.5}, which indicates the 97.5th percentile of utterance duration (in seconds). We use this number for each language to drop superfluous utterances with a duration greater than P97.5 to avoid CUDA errors during training. 

\subsection{Train/Dev \& Test Splits}
The data was originally divided into train/development and test sets. We used the provided training set for model learning and the validation set to tune hyperparameters and monitor performance. Our access to the gold labels for the test data set was limited, preventing us from being able to evaluate the model on the actual test data. To adapt to this, we held out 45 minutes of data from the validation set, serving as a replacement for the gold labels, and got an estimate of the model's performance. By enacting this approach, we ensured the distinct use of a train/dev and test split while gathering meaningful results.

For reproducibility, we release the exact utterances used across all language splits (for training, validation, and testing) here\footnote{\url{https://github.com/vitthal-bhandari/low-resource-asr/tree/master/results/splits}}.