Addressing challenges in speaker anonymization to maintain utility while ensuring privacy of pathological speech.

ABSTRACT:

Background

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.

Methods

This study investigates anonymization's impact on pathological speech across over 2700 speakers from multiple German institutions, focusing on privacy, pathological utility, and demographic fairness. We explore both deep-learning-based and signal processing-based anonymization methods.

Results

We do

SUBMITTER: Tayebi Arasteh S 

PROVIDER: S-EPMC11424628 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature