This paper investigates how OpenAI’s Whisper model processes dysarthric speech by probing its internal acoustic feature representations. Utilizing the TORGO database, we analyzed Whisper’s capability to encode significant acoustic features specific to dysarthric speech across its encoding layers. Our findings reveal that initial layers are particularly effective in capturing distinct features, while deeper layers show generalized representations. Despite this, Whisper’s zero-shot performance in distinguishing dysarthric speech severity levels is noteworthy. We employed a series of probing tasks tailored to dysarthric speech characteristics to pinpoint specific features and their transformation across the model’s layers. This study highlights Whisper’s potential in handling atypical speech patterns without fine-tuning, paving the way for further research into the interpretability and application of transformer-based models in medical and assistive technologies. We discuss the implications of these results for enhancing transparency, reliability, and safe AI integration in healthcare.