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arxiv: 2606.05561 · v1 · pith:4KSQZC5F · submitted 2026-06-04 · cs.CL · cs.AI

InfoShield: Privacy-Preserving Speech Representations for Mental Health Screening via Information-Theoretic Optimization

Xueyang Wu , Siyuan Liu , Kezhuo Yang , Guang Ling This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-06-28 02:01 UTCgrok-4.3pith:4KSQZC5Frecord.jsonopen to challenge →

classification cs.CL cs.AI
keywords privacy-preserving speechmental health screeningmutual informationdepression detectionTimeAwareMINEgender inferenceage inferenceinformation-theoretic optimization
0
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The pith

InfoShield minimizes mutual information between speech representations and sensitive attributes like gender and age while preserving depression classification performance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to resolve the tension between accurate speech-based depression screening and users' privacy concerns over demographic leakage. It does so by training representations that carry less mutual information with attributes such as gender and age. A reader would care because current privacy methods either fail against new attacks or degrade diagnostic utility through blanket noise. The work introduces a specialized mutual-information estimator suited to sequential audio and reports concrete drops in attribute inference alongside only modest loss in screening accuracy.

Core claim

InfoShield identifies that standard MINE estimators fail on sequential speech because of temporal-static misalignment and therefore introduces TimeAwareMINE, which uses cross-modal attention to align acoustic frames with attribute embeddings. By minimizing the resulting mutual-information estimates between the learned representations and sensitive attributes, the method reduces gender inference accuracy from 92.6% to 55.5% and age inference from 55.7% to 30.3%, while the depression-detection F1 score falls only 6% to 0.784, exceeding the prior state-of-the-art F1 of 0.723.

What carries the argument

TimeAwareMINE estimator with cross-modal attention that aligns acoustic frames to attribute embeddings in order to produce reliable mutual-information estimates on sequential speech data.

If this is right

  • Gender inference accuracy on the protected representations falls to 55.5%.
  • Age inference accuracy falls to 30.3%.
  • Depression screening F1 reaches 0.784, outperforming the prior best reported result of 0.723.
  • The utility penalty remains limited to a 6% F1 reduction relative to an unprotected baseline.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same mutual-information minimization approach could be tested on other sequential modalities such as video or physiological signals.
  • The reported privacy gains rest on the specific classifiers used for evaluation; stronger or future attacks might recover more information.
  • Combining InfoShield with differential privacy mechanisms might yield additive protection without further utility loss.

Load-bearing premise

Lowering inference accuracy on the tested classifiers is treated as evidence of meaningful privacy protection against realistic adversaries.

What would settle it

A new attribute classifier or adaptive attack that recovers gender or age labels at high accuracy from the released representations would falsify the privacy claim.

Figures

Figures reproduced from arXiv: 2606.05561 by Guang Ling, Kezhuo Yang, Siyuan Liu, Xueyang Wu.

Figure 1
Figure 1. Figure 1: InfoShield architecture. Left: speech encoder pro￾duces latent representations Z for depression classification. Right: TimeAwareMINE quantifies privacy leakage via cross￾modal attention with transcript embeddings. Three loss terms jointly optimize the framework. Red dashed arrows indicate gradient backpropagation. specific attack models. Differential Privacy provides formal guarantees through global noise … view at source ↗
read the original abstract

Speech-based mental health screening offers scalable depression detection, yet clinical deployment faces a significant barrier: users' privacy concerns about demographic information exposure. Current techniques struggle to resolve this conflict. Adversarial training often fails against unseen threats, whereas Differential Privacy tends to compromise diagnostic performance by injecting noise across all features. This paper presents InfoShield, which minimizes mutual information between speech representations and sensitive attributes while preserving depression classification accuracy. We identify that standard MINE estimators struggle with sequential speech due to temporal-static misalignment, and introduce TimeAwareMINE with cross-modal attention to align acoustic frames with attribute embeddings. Experiments on the Androids Corpus show InfoShield reduces gender inference from 92.6\% to 55.5\% and age inference from 55.7\% to 30.3\% with limited utility loss (6\% F1 reduction), achieving F1=0.784 compared to prior SOTA's 0.723.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces InfoShield, which minimizes mutual information between learned speech representations and sensitive attributes (gender, age) via an information-theoretic objective while preserving utility for depression classification. It proposes TimeAwareMINE, an extension of MINE using cross-modal attention to address temporal-static misalignment in sequential speech, and reports on the Androids Corpus that the method reduces gender inference from 92.6% to 55.5% and age inference from 55.7% to 30.3% with a 6% F1 drop, reaching F1=0.784 versus prior SOTA of 0.723.

Significance. If the privacy claims prove robust, the work would offer a useful alternative to adversarial training and differential privacy for speech-based mental health applications by directly optimizing an information-theoretic privacy-utility tradeoff. The domain-specific estimator for sequential data is a targeted contribution, and the reported gains over SOTA on a real corpus indicate potential practical value if the evaluation holds.

major comments (2)
  1. [Experiments] Experimental evaluation: privacy reductions are demonstrated only against the fixed attribute classifiers described; no experiments test whether an adversary aware of the InfoShield objective, with access to the data distribution, or using stronger architectures (e.g., larger transformers or ensembles) can recover substantially more attribute information. This is load-bearing for the central privacy claim.
  2. [Method] TimeAwareMINE section: the estimator is introduced to handle sequential speech, yet no calibration against known MI values or head-to-head comparison with alternatives (InfoNCE, NWJ) on held-out sequential speech data is reported, leaving the accuracy of the minimized MI values unverified.
minor comments (1)
  1. [Abstract] Abstract: the 6% F1 reduction is stated without an explicit baseline F1 value, making the magnitude of the utility loss harder to interpret directly from the abstract alone.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment point by point below, with honest assessment of what revisions are feasible.

read point-by-point responses

  1. Referee: [Experiments] Experimental evaluation: privacy reductions are demonstrated only against the fixed attribute classifiers described; no experiments test whether an adversary aware of the InfoShield objective, with access to the data distribution, or using stronger architectures (e.g., larger transformers or ensembles) can recover substantially more attribute information. This is load-bearing for the central privacy claim.

    Authors: We agree this is a substantive point for the privacy claims. Our reported results demonstrate reductions against the attribute classifiers used in the evaluation protocol, which is standard for such work, and the mutual information objective provides a distribution-level bound independent of any particular adversary. However, we did not evaluate against adaptive adversaries or stronger models. In revision we will add a limitations paragraph discussing this gap and include new results with an ensemble of attribute classifiers to partially address the concern. revision: partial

  2. Referee: [Method] TimeAwareMINE section: the estimator is introduced to handle sequential speech, yet no calibration against known MI values or head-to-head comparison with alternatives (InfoNCE, NWJ) on held-out sequential speech data is reported, leaving the accuracy of the minimized MI values unverified.

    Authors: The referee is correct that no such calibration or comparison is reported. While the cross-modal attention design directly targets the temporal-static misalignment issue in speech, the absence of verification against known MI values or other estimators (InfoNCE, NWJ) leaves the estimator's accuracy unconfirmed. We will add these experiments on held-out sequential speech data in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity: results presented as empirical outcomes without self-referential derivations

full rationale

The abstract and provided text describe an empirical method (InfoShield with TimeAwareMINE) evaluated on the Androids Corpus, reporting measured reductions in attribute inference accuracy and F1 scores. No equations, derivations, or load-bearing steps are shown that reduce claimed results to fitted inputs or self-citations by construction. The central claims rest on experimental measurements against reported classifiers rather than any mathematical identity or renamed ansatz.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only; no explicit free parameters, axioms, or invented entities are stated. The method implicitly relies on the assumption that mutual information minimization yields privacy and that the estimator is faithful, but these are not formalized.

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discussion (0)

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Reference graph

Works this paper leans on

28 extracted references · 9 canonical work pages · 3 internal anchors

  1. [1]

    Speech-based screening offers scalable, non-invasive depression detection as acoustic features encode clinically rel- evant biomarkers [3, 4]

    Introduction Depression affects approximately 4.4% of the global popula- tion [2]. Speech-based screening offers scalable, non-invasive depression detection as acoustic features encode clinically rel- evant biomarkers [3, 4]. However, clinical deployment faces a significant barrier: users’ privacy concerns about demographic information exposure. Studies s...

  2. [2]

    InfoShield: Privacy-Preserving Speech Representations for Mental Health Screening via Information-Theoretic Optimization

    Related Work Speech-Based Mental Health Screening.Speech analysis demonstrates significant potential for non-invasive depression detection [3, 4]. Recent deep learning approaches achieve com- petitive performance [1, 5], yet clinical deployment remains limited due to privacy concerns about demographic leakage [8]. Our work addresses this gap with privacy-...

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  3. [3]

    InfoShield Architecture Figure 1 illustrates the overall InfoShield framework

    Methodology 3.1. InfoShield Architecture Figure 1 illustrates the overall InfoShield framework. The in- put log-mel spectrogramXis processed by a Transformer en- coder to produce stochastic latent representationsZ. Three loss terms jointly optimize the network: (1) utility lossL utility for depression prediction, (2) VIB compression lossL VIB for reg- ula...

  4. [4]

    Experiments 4.1. Experimental Setup Clinical Dataset:The Androids Corpus [5] contains 228 recordings from 118 native Italian speakers (64 clinically di- agnosed with depression, 54 healthy controls). Similar to prior work [1], we use interview speech for ecological validity. For privacy evaluation, we extract gender and age groups 1 from clinical metadata...

  5. [5]

    By integrating VIB compression with TimeAwareMINE, we achieve utility-privacy balance for healthcare applications

    Conclusion This paper presents InfoShield, a unified information-theoretic framework addressing privacy concerns in speech-based men- tal health technologies. By integrating VIB compression with TimeAwareMINE, we achieve utility-privacy balance for healthcare applications. Summary of Contributions.Our work delivers three key advancements: (1)TimeAwareMINE...

  6. [6]

    All core research contributions were completed by the authors, in- cluding idea conception, initial drafting, related work analysis, code implementation, and data preprocessing

    Disclosure of Generative AI Use Generative AI tools were used solely for: (1) text polishing and language refinement to improve clarity; (2) code efficiency opti- mization suggestions; and (3) partial debugging assistance. All core research contributions were completed by the authors, in- cluding idea conception, initial drafting, related work analysis, c...

  7. [7]

    Thin slices of depression: Improving depression detection performance through data segmentation,

    R. Alsarrani, A. Esposito, and A. Vinciarelli, “Thin slices of depression: Improving depression detection performance through data segmentation,” inICASSP 2022-2022 IEEE Inter- national Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022, pp. 6257–6261

    2022

  8. [8]

    World mental health report: Transforming mental health for all,

    World Health Organization, “World mental health report: Transforming mental health for all,”World Health Organization,

  9. [9]

    Geneva: WHO, 2023 [Online]

    [Online]. Available: https://www.who.int/publications/i/ item/9789240049338

    work page arXiv

  10. [10]

    Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning,

    N. Cummins, A. Baird, and B. W. Schuller, “Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning,”Methods, vol. 151, pp. 41–54, 2018

    2018

  11. [11]

    V oice-based detec- tion of alzheimer’s disease using machine learning,

    D. M. Low, K. H. Bentley, and S. S. Ghosh, “V oice-based detec- tion of alzheimer’s disease using machine learning,”PLoS One, vol. 15, no. 12, p. e0244285, 2020

    2020

  12. [12]

    The androids corpus: A new publicly available benchmark for speech based depression detection,

    F. Tao, A. Esposito, and A. Vinciarelli, “The androids corpus: A new publicly available benchmark for speech based depression detection,”Depression, vol. 47, pp. 11–9, 2023

    2023

  13. [13]

    Adversarial-based training for asr-robust speaker verification and voice id-hiding,

    L. Fan, A. Sgroi, M. St-Hilaire, M. Cory, J. Du, and J. Glass, “Adversarial-based training for asr-robust speaker verification and voice id-hiding,” inProc. Interspeech 2019, 2019, pp. 2200–2204

    2019

  14. [14]

    Differential privacy,

    C. Dwork, “Differential privacy,” inInternational colloquium on automata, languages, and programming. Springer, 2006, pp. 1–12

    2006

  15. [15]

    Probing mental health information in speech foundation models,

    M. de Gennes, A. Lesage, M. Denais, X.-N. Cao, S. Chang, P. Van Remoortere, C. Dakhlia, and R. Riad, “Probing mental health information in speech foundation models,”arXiv preprint arXiv:2409.19042, 2024

    work page arXiv 2024

  16. [16]

    Deep neural network embeddings for text-independent speaker verification,

    D. Snyder, D. Garcia-Romero, D. Povey, and S. Khudanpur, “Deep neural network embeddings for text-independent speaker verification,” inProc. Interspeech 2017, 2017, pp. 999–1003

    2017

  17. [17]

    Speaker Anonymization Using X-vector and Neural Waveform Models

    F. Fang, X. Wang, J. Yamagishi, I. Echizen, M. Todisco, N. Evans, and J.-F. Bonastre, “Speaker anonymization using x-vector and neural waveform models,”arXiv preprint arXiv:1905.13561, 2019

    work page internal anchor Pith review Pith/arXiv arXiv 1905

  18. [18]

    Goodfellow, H

    M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” inProceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, Oct. 2016. [Online]. Available: http://dx.doi.org/10.1145/2976749.2978318

    work page doi:10.1145/2976749.2978318 2016

  19. [19]

    Federated learning with differen- tial privacy for end-to-end speech recognition,

    M. Pelikan, S. S. Azam, V . Feldman, J. Silovsky, K. Tal- war, T. Likhomanenkoet al., “Federated learning with differen- tial privacy for end-to-end speech recognition,”arXiv preprint arXiv:2310.00098, 2023

    work page arXiv 2023

  20. [20]

    The information bottle- neck method,

    N. Tishby, F. C. Pereira, and W. Bialek, “The information bottle- neck method,” inProceedings of the 37th Annual Allerton Con- ference on Communication, Control, and Computing. Citeseer, 2000, pp. 368–377

    2000

  21. [21]

    MINE: Mutual Information Neural Estimation

    M. I. Belghazi, A. Baratin, S. Rajeswar, S. Ozair, Y . Bengio, A. Courville, and R. D. Hjelm, “Mine: mutual information neural estimation,”arXiv preprint arXiv:1801.04062, 2018

    work page Pith review arXiv 2018

  22. [22]

    Club: A contrastive log-ratio upper bound of mutual information,

    P. Cheng, W. Hao, S. Dai, J. Liu, Z. Gan, and L. Carin, “Club: A contrastive log-ratio upper bound of mutual information,”Pro- ceedings of the 37th International Conference on Machine Learn- ing, pp. 1779–1788, 2020

    2020

  23. [23]

    Learning speaker representations with mutual information,

    M. Ravanelli and Y . Bengio, “Learning speaker representations with mutual information,” inInterspeech 2019, 2019, pp. 1153– 1157

    2019

  24. [24]

    On variational bounds of mutual information,

    B. Poole, S. Ozair, A. Van Den Oord, A. Alemi, and G. Tucker, “On variational bounds of mutual information,” inInternational conference on machine learning. PMLR, 2019, pp. 5171–5180

    2019

  25. [25]

    Univer- sal semantic disentangled privacy-preserving speech representa- tion learning,

    B. T. Vecino, S. Maji, A. Varier, A. Bonafonte, I. Valles, M. Owen, L. R¨adel, G. Strimel, S. Feyisetan, R. B. Chicoteet al., “Univer- sal semantic disentangled privacy-preserving speech representa- tion learning,”arXiv preprint arXiv:2505.13085, 2025

    work page arXiv 2025

  26. [26]

    Safeear: Con- tent privacy-preserving audio deepfake detection,

    X. Li, K. Li, Y . Zheng, C. Yan, X. Ji, and W. Xu, “Safeear: Con- tent privacy-preserving audio deepfake detection,” inProceedings of the 2024 on ACM SIGSAC Conference on Computer and Com- munications Security, 2024, pp. 3585–3599

    2024

  27. [27]

    T. M. Cover,Elements of information theory. John Wiley & Sons, 1999

    1999

  28. [29]

    Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

    [Online]. Available: http://arxiv.org/abs/1908.10084

    work page internal anchor Pith review Pith/arXiv arXiv 1908