\begin{abstract}
We conduct Automatic speech recognition (ASR) experiments on the Common Voice Spontaneous Speech dataset by Mozilla Data Collective, consisting of 21 low-resource languages across four continents of the world. We fine-tune popular multilingual speech models on all languages of this dataset, and observe that while a single-best-model solution doesn't exist, the Massively Multilingual Speech model and Whisper achieve superior performance on certain languages. Through $n$-gram language modeling decoding experiments, we observe a significant improvement in error rate over greedy decoding by up to $27.3$\%. We follow our experiments with a close linguistic error analysis of the best performing models on Scots (sco) and Nubi (kcn) - two of the languages in our dataset, with very little prior audio and text modeling research. We highlight the morphosyntactic errors induced during speech recognition and perform a holistic analysis of these languages. We finally advocate for the importance of building efficient and accurate ASR tools for modeling speech in endangered languages with scarce resources, and their applications to language revitalization, language learning assistance, and accessibility. The code can be found at \url{https://github.com/vitthal-bhandari/low-resource-asr/}
\end{abstract}