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REVIEW 2 major objections 2 minor 35 references

Audio-visual Contrastive Alignment for Diffusion-based Visual-conditioned Speech Enhancement

T0 review · 2 major / 2 minor · reviewed 2026-06-26 · grok-4.3

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Pith's one-line read Augmenting the diffusion training objective with a contrastive audio-visual loss improves visual-conditioned speech enhancement.

desk verdict The paper adds a contrastive audio-visual term to diffusion AVSE training and claims gains on enhancement metrics, but the abstract supplies no numbers or ablations so the size and source of the improvement remain unclear. read the letter →

arxiv 2606.23712 v1 pith:A7USEK4E submitted 2026-06-16 eess.SP cs.AI

Colombe Mboungou (MULTISPEECH) , Mostafa Sadeghi (MULTISPEECH) , Jean-Eudes Ayilo (MULTISPEECH) , Romain Serizel (MULTISPEECH) This is my paper
classification eess.SPcs.AI
keywords audio-visualspeechenhancementdiffusionmodelscontrastivealignmentcross-modalfusionvisualconditioningposteriorsampling
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper establishes that adding a contrastive audio-visual loss during training of a visual-conditioned diffusion model for speech enhancement encourages stronger use of visual cues such as lip movements. This addition occurs without altering the posterior sampling procedure used at inference. A sympathetic reader would care because it targets better recovery of speech in noisy settings like crowds or traffic, with gains especially visible when noise levels are high. Experiments confirm improvements in suppressing interference, reconstructing the original signal, and listener-perceived quality on both matched and mismatched test conditions.

What carries the argument

Contrastive audio-visual loss term added to the diffusion training objective to enforce cross-modal alignment in visual feature fusion via cross-attention.

What would settle it

An experiment showing no gain or a drop in enhancement metrics when the contrastive loss is added, or a measurement showing the alignment does not correlate with sampling performance, would falsify the central claim.

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Extended reading notes

Core claim

The authors establish that augmenting the diffusion training objective with a contrastive audio-visual loss encourages stronger use of visual information in a cross-attention conditioned diffusion model while keeping the posterior sampling framework unchanged, leading to consistent gains in interference suppression, signal reconstruction, and perceptual quality across matched and mismatched test data, with the largest improvements at low SNRs.

Load-bearing premise

The contrastive loss produces stronger cross-modal alignment that actually helps the downstream posterior sampling task rather than simply trading off against the diffusion objective.

Editorial extensions

If this is right

  • Consistent gains in interference suppression across test conditions.
  • Improved signal reconstruction quality.
  • Higher perceptual quality in the enhanced output.
  • Largest benefits appear at low signal-to-noise ratios.
  • Improvements hold on both matched and mismatched test data.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same contrastive alignment step could be tested in other diffusion-based audio-visual tasks such as separation or recognition.
  • Explicit alignment losses might lower the need for perfectly paired training data in multimodal generative models.
  • Combining the loss with additional conditioning signals could be explored as a direct extension.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes augmenting the diffusion training objective of a visual-conditioned speech enhancement model with an auxiliary contrastive audio-visual loss. This is intended to encourage stronger cross-modal alignment while leaving the posterior sampling procedure unchanged. Experiments on matched and mismatched test sets report consistent gains in interference suppression, signal reconstruction, and perceptual quality metrics, with the largest improvements observed at low SNRs.

Significance. If the reported gains are attributable to improved visual conditioning rather than incidental training effects, the approach supplies a lightweight, modular way to strengthen multi-modal conditioning inside existing diffusion pipelines for audio-visual speech enhancement. The public release of code is a clear asset for reproducibility.

major comments (2)
  1. [Section 3.2] Section 3.2 and Eq. (combined objective): the weighting hyper-parameter between the diffusion loss and the contrastive term is load-bearing for the central claim; without an ablation across a range of values or a sensitivity analysis, it remains unclear whether the reported improvements are robust or specific to a tuned balance.
  2. [Table 2] Table 2 (low-SNR rows): the largest gains are claimed at low SNRs, yet the table does not report error bars or statistical tests across multiple random seeds; this weakens the assertion of consistent improvement.
minor comments (2)
  1. [Abstract] The abstract would be strengthened by including at least one quantitative result (e.g., PESQ or STOI delta) to support the performance claims.
  2. [Section 3] Notation for the visual feature extractor and the contrastive projection heads should be introduced once and used consistently throughout Sections 3 and 4.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for minor revision. We address each major comment below and will update the manuscript accordingly to strengthen the presentation of our results.

read point-by-point responses

  1. Referee: [Section 3.2] Section 3.2 and Eq. (combined objective): the weighting hyper-parameter between the diffusion loss and the contrastive term is load-bearing for the central claim; without an ablation across a range of values or a sensitivity analysis, it remains unclear whether the reported improvements are robust or specific to a tuned balance.

    Authors: We agree that the weighting hyper-parameter λ is important for the combined objective. In the submitted manuscript, λ was set to 0.1 following validation-set tuning, but a full sensitivity analysis was not included. In the revised version we will add an ablation table varying λ over [0.01, 0.05, 0.1, 0.5, 1.0] and report the resulting PESQ, STOI, and SI-SDR values on both matched and mismatched test sets to demonstrate that the reported gains are not overly sensitive to the precise choice of λ. revision: yes

  2. Referee: [Table 2] Table 2 (low-SNR rows): the largest gains are claimed at low SNRs, yet the table does not report error bars or statistical tests across multiple random seeds; this weakens the assertion of consistent improvement.

    Authors: We acknowledge that Table 2 currently lacks error bars and statistical tests. To address this, the revised manuscript will include results averaged over five independent random seeds, reporting mean ± standard deviation for all metrics. We will also add a brief note on paired t-tests (or Wilcoxon signed-rank tests) between the baseline and proposed models at the lowest SNR conditions to quantify the statistical significance of the observed improvements. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity in derivation chain

full rationale

The paper presents an empirical method: augmenting a diffusion training objective with an auxiliary contrastive audio-visual loss term while leaving the posterior sampling pipeline unmodified. The central claim rests on experimental results across matched/mismatched test sets rather than any derivation that reduces a reported quantity to a fitted parameter or self-citation by construction. No equations, uniqueness theorems, or ansatzes are invoked that collapse the improvement to the input loss definition itself. The argument is self-contained against external benchmarks and does not rely on load-bearing self-citations.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the central claim rests on the unverified assumption that the added loss term improves visual utilization without side effects on the diffusion prior.

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Cite this review

Pith. "Pith review of Audio-visual Contrastive Alignment for Diffusion-based Visual-conditioned Speech Enhancement." pith.science (2026). https://pith.science/paper/A7USEK4E

@misc{pith2026260623712,
  author       = {Pith},
  title        = {Pith review of: Audio-visual Contrastive Alignment for Diffusion-based Visual-conditioned Speech Enhancement},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/A7USEK4E}},
  note         = {Machine review of arXiv:2606.23712}
}
read the original abstract

Audio-visual speech enhancement (AVSE) exploits visual cues such as lip movements to recover speech in noisy environments. Recent work introduced diffusion-based unsupervised AVSE, where a speech diffusion model conditioned on visual features via cross-attention is trained and used as a data-driven prior for posterior sampling-based speech enhancement. Despite promising performance over its audio-only counterpart, the impact of explicitly enforcing cross-modal alignment in the fusion remains unclear. In this work, we propose to augment the diffusion training objective with a contrastive audio-visual loss to encourage stronger use of visual information while keeping the posterior sampling framework unchanged. Experiments across matched and mismatched test data show consistent improvements in interference suppression, signal reconstruction, and perceptual quality, with the largest gains at low SNRs. Code is available at https://github.com/ cexauce/AV-CA-DiffUSE

Figures

Figures reproduced from arXiv: 2606.23712 by the authors.

Figure 1
Figure 1. Audio-visual score model with contrastive alignment. joint audio-visual embedding space. Consequently, the model may under-utilize visual information when the audio signal alone provides a strong prior. This motivates the introduction of an explicit contrastive audio-visual alignment objective, de￾scribed in the following subsection, which encourages richer and more globally consistent audio-visual representations. … view at source ↗
Figure 2
Figure 2. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

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Reference graph

Works this paper leans on

35 extracted references · 5 canonical work pages

  1. [1]

    Recent deep learn- ing approaches have significantly improved performance [1–3], with generative modeling frameworks emerging as a powerful direction

    Introduction Speech enhancement (SE) aims to recover clean speech from noisy observations, a long-standing problem in speech process- ing that remains challenging under low signal-to-noise ratio (SNR) conditions and non-stationary noise. Recent deep learn- ing approaches have significantly improved performance [1–3], with generative modeling frameworks em...

  2. [2]

    Audio-visual Contrastive Alignment for Diffusion-based Visual-conditioned Speech Enhancement

    Diffusion-based unsupervised A VSE We briefly review the diffusion-based unsupervised SE base- line, A V-DiffUSEEN, which combines the audio-visual training pipeline of A V-UDiffSE+ [8] with an extended unsupervised SE inference scheme, called DiffUSEEN [13]. The SE problem is considered in the short-time Fourier transform (STFT) domain, where the observe...

    work page Pith review arXiv 2026

  3. [3]

    1, describing how the audio-visual con- trastive loss is computed and the motivation behind it

    Audio-visual contrastive alignment This section presents our proposed contrastive alignment frame- work, depicted in Fig. 1, describing how the audio-visual con- trastive loss is computed and the motivation behind it. 3.1. Limitations of baseline audio-visual fusion In A V-UDiffSE+ [8], visual features are injected into the score network via a cross-atten...

  4. [4]

    As baselines, we con- sider A V-DiffUSEEN, with cross-attention fusion [8, 13], the audio-only version, AO-DiffUSEEN [13], and the supervised- generative FlowA VSE model [7]

    Experimental setup We analyze how contrastive cross-modal alignment affects in- terference suppression and robustness in diffusion-based A VSE across input SNR and data mismatch. As baselines, we con- sider A V-DiffUSEEN, with cross-attention fusion [8, 13], the audio-only version, AO-DiffUSEEN [13], and the supervised- generative FlowA VSE model [7]. We ...

  5. [5]

    Matched condition: TCD-DEMAND Under matched conditions (Table 1), the proposed model im- proves all metrics compared to A V-DiffUSEEN

    Results 5.1. Matched condition: TCD-DEMAND Under matched conditions (Table 1), the proposed model im- proves all metrics compared to A V-DiffUSEEN. In particular, we observe around +5 dB gain in SI-SIR, indicating improved suppression of noise interference. This translates into a +2.4 dB improvement in SI-SDR, reflecting a better overall quality of recons...

  6. [6]

    During the pretraining of the visual-conditioned speech diffusion model, we augment the denoising score matching ob- jective with a contrastive audio-visual alignment loss

    Conclusion We studied the role of explicit cross-modal alignment in diffusion-based unsupervised audio-visual speech enhance- ment. During the pretraining of the visual-conditioned speech diffusion model, we augment the denoising score matching ob- jective with a contrastive audio-visual alignment loss. Experi- ments show that better aligned embeddings le...

  7. [7]

    Acknowledgment Experiments in this work were conducted using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER, several universities, and other organizations (see https://www.grid5000.fr)

  8. [8]

    Generative AI tools were only used to edit and polish some portions of the manuscript

    Generative AI Use Disclosure All scientific content, methodology, experiments, analyses, in- terpretations, and conclusions were developed, verified, and ap- proved by the authors, who take full responsibility for the con- tents of the paper. Generative AI tools were only used to edit and polish some portions of the manuscript

Show all 35 references
  1. [9]

    SEGAN: Speech enhancement generative adversarial network,

    S. Pascual, A. Bonafonte, and J. Serr `a, “SEGAN: Speech enhancement generative adversarial network,”Interspeech, p. 3642, 2017

    2017

  2. [10]

    Conv-TasNet: Surpassing ideal time– frequency magnitude masking for speech separation,

    Y . Luo and N. Mesgarani, “Conv-TasNet: Surpassing ideal time– frequency magnitude masking for speech separation,” IEEE/ACM transactions on audio, speech, and language processing, vol. 27, no. 8, pp. 1256–1266, 2019

    2019

  3. [11]

    DCCRN: Deep complex convolution recurrent network for phase-aware speech enhancement,

    Y . Hu, Y . Liu, S. Lv, M. Xing, S. Zhang, Y . Fu, J. Wu, B. Zhang, and L. Xie, “DCCRN: Deep complex convolution recurrent network for phase-aware speech enhancement,”Interspeech, 2020

    2020

  4. [12]

    Speech enhancement and dereverberation with diffusion-based gen- erative models,

    J. Richter, S. Welker, J.-M. Lemercier, B. Lay, and T. Gerkmann, “Speech enhancement and dereverberation with diffusion-based gen- erative models,” IEEE/ACM Transactions on Audio, Speech, and Lan- guage Processing, 2023

    2023

  5. [13]

    The conversation: Deep audio-visual speech enhancement,

    T. Alfouras, J. Chung, and A. Zisserman, “The conversation: Deep audio-visual speech enhancement,” in Interspeech, 2018

    2018

  6. [14]

    Audio-visual speech enhancement using multimodal deep con- volutional neural networks,

    J.-C. Hou, S.-S. Wang, Y .-H. Lai, Y . Tsao, H.-W. Chang, and H.-M. Wang, “Audio-visual speech enhancement using multimodal deep con- volutional neural networks,”IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 2, no. 2, pp. 117–128, 2018

    2018

  7. [15]

    FlowA VSE: Efficient audio-visual speech enhancement with conditional flow matching,

    C. Jung, S. Lee, J.-H. Kim, and J. S. Chung, “FlowA VSE: Efficient audio-visual speech enhancement with conditional flow matching,” in Interspeech, 2024, pp. 2210–2214

    2024

  8. [16]

    Diffusion-based unsupervised audio-visual speech enhancement,

    J.-E. Ayilo, M. Sadeghi, R. Serizel, and X. Alameda-Pineda, “Diffusion-based unsupervised audio-visual speech enhancement,” in IEEE International Conference on Acoustics, Speech and Signal Pro- cessing (ICASSP), 2025

    2025

  9. [17]

    An overview of deep-learning-based audio-visual speech enhancement and separation,

    D. Michelsanti, Z.-H. Tan, S.-X. Zhang, Y . Xu, M. Yu, D. Yu, and J. Jensen, “An overview of deep-learning-based audio-visual speech enhancement and separation,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 1368–1396, 2021

    2021

  10. [18]

    Learning transfer- able visual models from natural language supervision,

    A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transfer- able visual models from natural language supervision,” in Interna- tional conference on machine learning (ICML) . PmLR, 2021, pp. 8748–8763

    2021

  11. [19]

    CLAP: Learn- ing audio concepts from natural language supervision,

    B. Elizalde, S. Deshmukh, M. Al Ismail, and H. Wang, “CLAP: Learn- ing audio concepts from natural language supervision,” in IEEE In- ternational Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023, pp. 1–5

    2023

  12. [20]

    SA V-SE: Scene-aware audio-visual speech enhancement with selective state space model,

    X. Qian, J. Gao, Y . Zhang, Q. Zhang, H. Liu, L. P. G. Perera, and H. Li, “SA V-SE: Scene-aware audio-visual speech enhancement with selective state space model,”IEEE Journal of Selected Topics in Signal Processing, vol. 19, no. 4, pp. 623–634, 2025

    2025

  13. [21]

    Diffusion-based frameworks for unsupervised speech enhancement,

    J.-E. Ayilo, M. Sadeghi, R. Serizel, and X. Alameda-Pineda, “Diffusion-based frameworks for unsupervised speech enhancement,” arXiv preprint arXiv:2601.09931, 2026

    2026 arXiv

  14. [22]

    Score-based generative modeling through stochastic differ- ential equations,

    Y . Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differ- ential equations,” in International Conference on Learning Represen- tations (ICLR), 2021

    2021

  15. [23]

    Nonnegative matrix factor- ization with the itakura-saito divergence: With application to music analysis,

    C. F ´evotte, N. Bertin, and J.-L. Durrieu, “Nonnegative matrix factor- ization with the itakura-saito divergence: With application to music analysis,” Neural computation, vol. 21, no. 3, pp. 793–830, 2009

    2009

  16. [24]

    Tweedie’s formula and selection bias,

    B. Efron, “Tweedie’s formula and selection bias,”Journal of the Amer- ican Statistical Association, vol. 106, no. 496, pp. 1602–1614, 2011

    2011

  17. [25]

    Deep residual learning for im- age recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for im- age recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) , 2016, pp. 770–778

    2016

  18. [26]

    Learning audio- visual speech representation by masked multimodal cluster predic- tion,

    B. Shi, W.-N. Hsu, K. Lakhotia, and A. Mohamed, “Learning audio- visual speech representation by masked multimodal cluster predic- tion,” arXiv preprint arXiv:2201.02184, 2022

    2022

  19. [27]

    Representation learning with con- trastive predictive coding,

    A. v. d. Oord, Y . Li, and O. Vinyals, “Representation learning with con- trastive predictive coding,”arXiv preprint arXiv:1807.03748, 2018

    2018 arXiv

  20. [28]

    TCD-TIMIT: An audio-visual corpus of con- tinuous speech,

    N. Harte and E. Gillen, “TCD-TIMIT: An audio-visual corpus of con- tinuous speech,” IEEE Transactions on Multimedia, vol. 17, no. 5, pp. 603–615, 2015

    2015

  21. [29]

    The diverse environments multi- channel acoustic noise database (DEMAND): A database of multi- channel environmental noise recordings,

    J. Thiemann, N. Ito, and E. Vincent, “The diverse environments multi- channel acoustic noise database (DEMAND): A database of multi- channel environmental noise recordings,” in Proceedings of Meetings on Acoustics, vol. 19, no. 1. AIP Publishing, 2013

    2013

  22. [30]

    LRS3-TED: a large-scale dataset for visual speech recognition,

    T. Afouras, J. S. Chung, and A. Zisserman, “LRS3-TED: a large-scale dataset for visual speech recognition,” arXiv preprint arXiv:1809.00496, 2018

    2018 arXiv

  23. [31]

    NTCD-TIMIT: A new database and base- line for noise-robust audio-visual speech recognition

    A. H. Abdelaziz et al. , “NTCD-TIMIT: A new database and base- line for noise-robust audio-visual speech recognition.” in Interspeech, 2017, pp. 3752–3756

    2017

  24. [32]

    SDR–half- baked or well done?

    J. Le Roux, S. Wisdom, H. Erdogan, and J. R. Hershey, “SDR–half- baked or well done?” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019

    2019

  25. [33]

    Performance measure- ment in blind audio source separation,

    E. Vincent, R. Gribonval, and C. F ´evotte, “Performance measure- ment in blind audio source separation,” IEEE Transactions on Au- dio, Speech, and Language Processing , vol. 14, no. 4, pp. 1462–1469, 2006

    2006

  26. [34]

    Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs,

    A. W. Rix, J. G. Beerends, M. P. Hollier, and A. P. Hekstra, “Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs,” in IEEE international conference on acoustics, speech, and signal processing. Proceedings ...

    2001

  27. [35]

    An algo- rithm for intelligibility prediction of time–frequency weighted noisy speech,

    C. H. Taal, R. C. Hendriks, R. Heusdens, and J. Jensen, “An algo- rithm for intelligibility prediction of time–frequency weighted noisy speech,”IEEE Transactions on Audio, Speech, and Language Process- ing, vol. 19, no. 7, pp. 2125–2136, February 2011

    2011

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