\section{Conclusion}
In this work, we benchmarked MMS, XLS-R, and Whisper on 21 low-resource languages from Mozilla Common Voice Spontaneous Speech and analyzed both model-level trends and language-specific errors. MMS delivered the strongest overall performance, while XLS-R achieved the largest relative gains from n-gram LM decoding, with improvements up to 27.3\% \texttt{WER} over greedy decoding. Across models, 3-gram and 4-gram decoding consistently outperformed unigram decoding, confirming that explicit n-gram LM integration is crucial for stronger CTC ASR. Our linguistic analysis of Scots and Nubi further showed recurring substitution, deletion, and insertion patterns tied to orthography, phonology, and punctuation. In Nubi, severe insertion-heavy outputs suggest transcription instability under low-resource conditions. Our findings reinforce that careful decoding and language-aware analysis are essential for robust ASR in endangered language settings, and for practical revitalization and accessibility tools development.  