{"doi":"10.1002/alz.70032","title":"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 dementia (DE). RESULTS: With the top 20 acoustic features, our framework achieved classification accuracies of 62.2% (EER: 0.3335) on the FHS dataset for NC, MCI, and DE differentiation, and 63.7% (EER: 0.1796) on the DBD dataset for NC and MCI differentiation, using obfuscated speech files. DISCUSSION: Our results demonstrate the feasibility of privacy-preserving voice markers, offering a scalable solution for voice-based cognitive assessments. HIGHLIGHTS: 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.","journal":"Alzheimer s & Dementia","year":2025,"id":538082,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9533,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":990029,"name":"Nauman Dawalatabad","orcid":"0000-0003-1592-6300","position":1,"is_corresponding":false},{"id":227469,"name":"Cody Karjadi","orcid":"0000-0001-9199-8723","position":2,"is_corresponding":false},{"id":471172,"name":"James Glass","orcid":"0000-0002-3097-360X","position":3,"is_corresponding":false},{"id":227481,"name":"Rhoda Au","orcid":"0000-0001-7742-4491","position":4,"is_corresponding":false},{"id":227482,"name":"Vijaya B. Kolachalama","orcid":"0000-0002-5312-8644","position":5,"is_corresponding":false},{"id":1199577,"name":"Meysam Ahangaran","orcid":"0000-0002-9326-4800","position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":null,"created_at":"2026-07-19T02:52:16.891390Z","pmid":"40084735","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}