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.

我们提出了一种自学习方法，用于低资源环境下的自动语音识别（ASR）。通过在小语种如旁遮普语中生成高度精确的伪标签，我们的方法在四个真实语音数据集上相对提高了14.94%的词错误率，并在Common Voice旁遮普语数据集上取得了最佳结果。

一种低资源语音识别的新型自训练方法