The Impact of Speech Anonymization on Pathology and Its Limits
Soroosh Tayebi Arasteh Tomas Arias-Vergara Paula Andrea Perez-Toro Tobias Weise Kai Packhaeuser Maria Schuster Elmar Noeth Andreas Maier Seung Hee Yang
Integration of speech into healthcare has intensified privacy concerns due to
its potential as a non-invasive biomarker containing individual biometric
information. In response, speaker anonymization aims to conceal personally
identifiable information while retaining crucial linguistic content. However,
the application of anonymization techniques to pathological speech, a critical
area where privacy is especially vital, has not been extensively examined. This
study investigates anonymization's impact on pathological speech across over
2,700 speakers from multiple German institutions, focusing on privacy,
pathological utility, and demographic fairness. We explore both training-based
and signal processing-based anonymization methods, and document substantial
privacy improvements across disorders-evidenced by equal error rate increases
up to 1933%, with minimal overall impact on utility. Specific disorders such as
Dysarthria, Dysphonia, and Cleft Lip and Palate experienced minimal utility
changes, while Dysglossia showed slight improvements. Our findings underscore
that the impact of anonymization varies substantially across different
disorders. This necessitates disorder-specific anonymization strategies to
optimally balance privacy with diagnostic utility. Additionally, our fairness
analysis revealed consistent anonymization effects across most of the
demographics. This study demonstrates the effectiveness of anonymization in
pathological speech for enhancing privacy, while also highlighting the
importance of customized approaches to account for inversion attacks.