自动匿名化会显著影响病理语音的感知质量，但临床评估仍可保持准确。

Automatic anonymization is increasingly used to enable ethical sharing of clinical speech, yet its perceptual and clinical consequences remain undercharacterized. We present a human-centered evaluation of automatically anonymized pathological speech, using a structured protocol with ten native and non-native German listeners spanning clinical and signal-processing expertise. The cohort comprised 180 German speakers from CLP, Dysarthria, Dysglossia, Dysphonia, and adult and child controls. Each original recording and its automatically-anonymized counterpart was evaluated on four tasks: zero-shot Turing-style discrimination, few-shot discrimination after brief familiarization, 5-point quality rating, and 4-point blinded clinical severity rating by a senior phoniatrician. Listeners detected anonymization at 91% zero-shot and 93% few-shot accuracy, with significant variation across disorders (p=0.008) that attenuated with familiarization. Perceived quality dropped by 30 ppts on a 0-100 scale (p<0.001), reorganizing the perceived-quality hierarchy across groups. Native language modulated detectability but not quality degradation, while domain expertise modulated quality degradation but not detectability, a double dissociation between the two listener attributes; speaker sex and age produced no detectable bias. Clinical severity ratings were preserved at near-perfect agreement in Dysarthria, Dysglossia, and Dysphonia (quadratic-weighted Cohen's kappa 0.87-0.94), with no recording shifting by more than one grade. Crucially, perceptual outcomes were decoupled from the standard computational privacy metric: the pathology with the strongest computational anonymization was the least perceptually conspicuous, and vice versa. These findings argue for disorder-stratified, listener-stratified, clinician-validated evaluation as the minimum standard for licensing anonymized speech for clinical use.