This work investigates the impact of automatic data augmentation on the generalization performance of CNN models on speech-based Parkinson’s disease classification. We propose a method that sequentially applies 12 voice-specific augmentations, selecting the most effective one based on performance. We use a pre-trained CNN as a feature extractor. To assess inter-dataset generalization, we conduct experiments where each dataset is used for training while the others are used for external validation. The results demonstrate that augmentation helps reduce the generalization gap, with specific augmentation strategies enhancing the accuracy by as much as 25% compared to the baseline setup. Despite the increase in computational complexity due to the proposed method, this study reinforces the importance of augmentation in domain adaptation for speech-based PD classification.