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.
View on arXiv@article{arasteh2025_2505.00409,
title={ Perceptual Implications of Automatic Anonymization in Pathological Speech },
author={ Soroosh Tayebi Arasteh and Saba Afza and Tri-Thien Nguyen and Lukas Buess and Maryam Parvin and Tomas Arias-Vergara and Paula Andrea Perez-Toro and Hiu Ching Hung and Mahshad Lotfinia and Thomas Gorges and Elmar Noeth and Maria Schuster and Seung Hee Yang and Andreas Maier },
journal={arXiv preprint arXiv:2505.00409},
year={ 2025 }
}