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This paper presents a fine-tuning approach for automatic speech recognition (ASR) that exploits linguistic similarities between dialects to address data scarcity and imbalance in dialectally rich, low-resource languages. The approach is evaluated on Occitan, a low-resource language composed of six main dialects with unevenly distributed resources. For the better-resourced dialects, Languedocien and Gascon, a two-step fine-tuning is applied to improve recognition performance by exploiting inter-dialectal information. For the less-represented dialects, we assess the effectiveness of knowledge transfer from the better-resourced variants, analyzing the model’s ability to generalize to sparsely represented dialects. The results show that leveraging linguistic proximity between dialects is an effective strategy to enhance ASR performance in low-resource settings with high dialectal variability.