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Obfuscation via pitch‐shifting for balancing privacy and diagnostic utility in voice‐based cognitive assessment
Meysam Ahangaran, Nauman Dawalatabad, Cody Karjadi, James Glass, Rhoda Au, Vijaya B. Kolachalama. Alzheimer s & Dementia, 2025-03-01
Topic: Risk, prevention and care in population studies
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. 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.
Key findings
- 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.
- 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.
Why it matters
This study developed a framework using pitch-shifting to protect speaker privacy while maintaining diagnostic utility in voice-based cognitive assessments, achieving classification accuracies of 62.2% and 63.7% on two datasets. The results demonstrate the feasibility of privacy-preserving voice markers for scalable cognitive assessments.
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Study details
- Participants128
- Effect size62.2%; 63.7%
- Evidence strengthLow
How this study scored 48/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Alzheimer s & Dementia) |
| Journal's citation record | 2 / 4 | Alzheimer s & Dementia: journal h-index 228 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 2 / 4 | 128 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 5 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 2.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 5 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Vijaya B. Kolachalama: h-index 36 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Massachusetts Institute of Technology: institution h-index 2,557 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40084735) |
| Free to read | 2 / 2 | Free to read |
| Total | 48 / 100 | — |
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Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Ahangaran, M., Dawalatabad, N., Karjadi, C., Glass, J., Au, R., & Kolachalama, V. B. (2025). Obfuscation via pitch‐shifting for balancing privacy and diagnostic utility in voice‐based cognitive assessment. Alzheimer s & Dementia. https://doi.org/10.1002/alz.70032
Vancouver
Ahangaran M, Dawalatabad N, Karjadi C, Glass J, Au R, Kolachalama VB. Obfuscation via pitch‐shifting for balancing privacy and diagnostic utility in voice‐based cognitive assessment. Alzheimer s & Dementia. 2025. doi:10.1002/alz.70032. PMID: 40084735.
BibTeX
@article{ahangaran2025-40084735,
author = {Ahangaran, Meysam and Dawalatabad, Nauman and Karjadi, Cody and Glass, James and Au, Rhoda and Kolachalama, Vijaya B.},
title = {{Obfuscation via pitch‐shifting for balancing privacy and diagnostic utility in voice‐based cognitive assessment}},
journal = {Alzheimer s \& Dementia},
year = {2025},
doi = {10.1002/alz.70032},
pmid = {40084735},
url = {https://doi.org/10.1002/alz.70032}
}
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