Recent automatic speech recognition (ASR) models struggle to correctly transcribe pathological speech but implicitly capture phonetic, syntactic, and semantic properties. Unlike state-of-the-art methods that rely on speech features based on time-consuming handcrafted speech transcription, we propose a simple and fully automated approach using ASR log-probabilities to quantify intelligibility in spontaneous speech of patients with Huntington’s disease. By linking this measure to clinical scores, we explore its potential as a scalable, lightweight biomarker for disease progression. Our findings suggest that ASR-derived uncertainty offers a novel, efficient, and noninvasive alternative for clinical assessment.