End-to-end automatic speech recognition (ASR) systems perform well for high-resource languages but struggle in low-resource settings due to the scarcity of usable training data. Recent advances in speech synthesis, particularly voice cloning, offer promising solutions by augmenting training corpora with synthetic speech. However, the impact of such synthetic data on ASR performance in low-resource scenarios remains underexplored. In this study, we take Hungarian as a case study to systematically evaluate how synthetic speech can enhance ASR under data-scarce conditions. We investigate multiple strategies for generating synthetic data and assess their effectiveness through extensive experiments. Our results show that incorporating synthetic speech can significantly reduce word error rates. Compared to the baseline, the largest relative improvement on the in-domain test set (BEA-Base) reaches 12.1% , while on the out-of-domain test set (CV), the relative reduction is 41.0%. These findings underscore the value of synthetic speech as a resource for improving ASR in low-resource languages.