  In this paper, we propose a self-training approach for automatic speech
recognition (ASR) for low-resource settings. While self-training approaches
have been extensively developed and evaluated for high-resource languages such
as English, their applications to low-resource languages like Punjabi have been
limited, despite the language being spoken by millions globally. The scarcity
of annotated data has hindered the development of accurate ASR systems,
especially for low-resource languages (e.g., Punjabi and M\=aori languages). To
address this issue, we propose an effective self-training approach that
generates highly accurate pseudo-labels for unlabeled low-resource speech. Our
experimental analysis demonstrates that our approach significantly improves
word error rate, achieving a relative improvement of 14.94% compared to a
baseline model across four real speech datasets. Further, our proposed approach
reports the best results on the Common Voice Punjabi dataset.
