Perceptual Implications of Automatic Anonymization in Pathological Speech

Automatic anonymization techniques are essential for ethical sharing of pathological speech data, yet their perceptual consequences remain understudied. This study presents the first comprehensive human-centered analysis of anonymized pathological speech, using a structured perceptual protocol involving ten native and non-native German listeners with diverse linguistic, clinical, and technical backgrounds. Listeners evaluated anonymized-original utterance pairs from 180 speakers spanning Cleft Lip and Palate, Dysarthria, Dysglossia, Dysphonia, and age-matched healthy controls. Speech was anonymized using state-of-the-art automatic methods (equal error rates in the range of 30-40%). Listeners completed Turing-style discrimination and quality rating tasks under zero-shot (single-exposure) and few-shot (repeated-exposure) conditions. Discrimination accuracy was high overall (91% zero-shot; 93% few-shot), but varied by disorder (repeated-measures ANOVA: p=0.007), ranging from 96% (Dysarthria) to 86% (Dysphonia). Anonymization consistently reduced perceived quality (from 83% to 59%, p<0.001), with pathology-specific degradation patterns (one-way ANOVA: p=0.005). Native listeners rated original speech slightly higher than non-native listeners (Delta=4%, p=0.199), but this difference nearly disappeared after anonymization (Delta=1%, p=0.724). No significant gender-based bias was observed. Critically, human perceptual outcomes did not correlate with automatic privacy or clinical utility metrics. These results underscore the need for listener-informed, disorder- and context-specific anonymization strategies that preserve privacy while maintaining interpretability, communicative functions, and diagnostic utility, especially for vulnerable populations such as children.