Obfuscation via pitch-shifting for balancing privacy and diagnostic utility in voice-based cognitive assessment.

ABSTRACT:

Introduction

Digital voice analysis is an emerging tool for differentiating cognitive states, but it poses privacy risks as automated systems may inadvertently identify speakers.

Methods

We developed a computational framework to evaluate the trade-off between voice obfuscation and cognitive assessment accuracy, using pitch-shifting as a representative method. This framework was applied to voice recordings from the Framingham Heart Study (FHS, n = 128) and the DementiaBank Delaware (DBD, n = 85) corpus, both featuring responses to neuropsychological tests. Speaker obfuscation was measured via equal error rate (EER), and diagnostic utility was assessed through machine learning models distinguishing cognitive states: normal cognition (NC), mild cognitive impairment (MCI), and d

SUBMITTER: Ahangaran M 

PROVIDER: S-EPMC12045024 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature