Outside the healthy bounds: reference intervals for population and individual-level medical speech analysis.

Reference intervals (RIs) are a well-established clinical tool for defining healthy bounds of laboratory measures. Recently, they have been applied to acoustic features extracted from speech to provide a transparent and disorder-agnostic perspective on symptomatic speakers. We evaluate the potential and limitations of RIs across three datasets: clean, laboratory recordings from people with multiple sclerosis (pwMS) and two noisy datasets targeting respiratory symptoms and work-related stress, collected under everyday-life conditions. At the population level, we compare the proportion of features outside the RI bounds between control and symptomatic groups, test noise augmentation of the RI-estimation corpus for improved robustness, and identify the most indicative acoustic features for each health condition. At the individual level, we derive a per-speaker deviation score that quantifies how far each speaker's acoustic profile deviates from the healthy reference, and assess its discriminative ability using the area under the receiver operating characteristic curve (AUC). Noise augmentation of the healthy reference corpus yields similar population-level separation; the continuous