This paper focuses on developing assistive technologies for dysarthria speech, which is challenging due to limited data. Recent advances in neural speech synthesis utilizing zero-shot speech replication techniques facilitate the generation of synthetic voices for data augmentation, but can introduce biases in dysarthria speech. Using the TORGO dataset, this study investigates the effectiveness of state-of-the-art F5-TTS in replicating dysarthria speech in terms of intelligibility, speaker similarity, and prosody preservation. Furthermore, fairness metrics such as unfair impact and parity difference are used to assess the imbalance between dysarthria severity levels.