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

We developed a computational framework using pitch-shifting and acoustic transformations to balance speaker privacy and diagnostic utility in voice-based cognitive assessments. We evaluated the framework on two independent datasets, Framingham Heart Study (FHS, n = 128) and DementiaBank Delaware (DBD, n = 85) corpus, assessing the trade-off between privacy (measured by equal error rate [EER]) and classification accuracy. Our framework achieved classification accuracies of 62.2% (EER: 0.3335) for distinguishing normal cognition (NC), mild cognitive impairment (MCI), and dementia in the FHS dataset and 63.7% (EER: 0.1796) for NC and MCI differentiation in the DBD dataset, using obfuscated speech files. Our framework demonstrates that pitch-shifting levels can preserve diagnostic utility while protecting speaker identity, offering a scalable and privacy-preserving solution.