No Audiogram: Leveraging Existing Scores for Personalized Speech Intelligibility Prediction

Boxuan Cao; Changgeng Mo; Haoshuai Zhou; Linkai Li; Shan Xiang Wang

REVIEW 3 major objections 5 minor 33 references

No Audiogram: Leveraging Existing Scores for Personalized Speech Intelligibility Prediction

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A listener's own past intelligibility scores, taken from a handful of known audio clips, predict their scores on new audio better than their audiogram does, and the paper builds a network, SSIPNet, that does exactly this.

desk verdict The few-shot idea is worth a look, but the headline result rests on a synthetic score recalibration that silently feeds audiogram info back into the target. read the letter →

arxiv 2506.02039 v1 pith:2FWMSECD submitted 2025-05-31 eess.AS cs.AIcs.SD

Haoshuai Zhou , Changgeng Mo , Boxuan Cao , Linkai Li , Shan Xiang Wang This is my paper

classification eess.AScs.AIcs.SD

keywords personalizedspeechintelligibilitypredictionsupportsamplesaudiogram-freefoundationmodelsfew-shotregressionClarityChallengehearingaidevaluationscore

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

This paper claims that a hearing-impaired listener's intelligibility scores on a few known audio clips are enough to predict their scores on new audio, and that this data-driven personalization beats the standard approach of conditioning on the listener's audiogram. On the Clarity Prediction Challenge dataset, the proposed network SSIPNet, built on a frozen speech foundation model, aggregates a small set of (audio, score) pairs into a listener embedding and outperforms the audiogram-based baseline on both RMSE and correlation. The practical stake is that hearing-aid fitting and speech-enhancement tuning could be personalized from listening history rather than from a clinical pure-tone test, which the paper argues misses cognitive and perceptual factors. Even one labeled sample per listener matches the audiogram baseline, and four or more yields the reported advantage, with best performance around sixteen support pairs.

What carries the argument

The load-bearing machinery is SSIPNet's two-stage design plus a dataset-decorrelation step. The Feature Extraction Module freezes the Whisper v3 encoder, temporally pools and transforms all 32 layer outputs, and injects the known intelligibility score as a linear-projected feature along the layer dimension, so the embedding is audio-and-score specific. The Support-Based Prediction Module treats each listener's support (audio, score) pairs as an order-independent set, applies global average pooling to their embeddings, concatenates the pooled listener embedding with the query embedding, and maps the result through a linear layer to a score. Before any of this, the audio-audiogram decorrelation step normalizes every audio to 65 dB SPL and recalibrates the ground-truth score using the level-intelligibility curves for conductive hearing loss from reference [22], S1 = S0 + CHL(L1) − CHL(L0), so that level-based audiogram leakage is removed from the training signal.

What would settle it

Re-run the entire experiment on the original CPC protocol without the calibration step, training SSIPNet on raw intelligibility scores and unnormalized audio, and compare against the audiogram baseline on the unchanged held-out test set; if the support-pair advantage shrinks or reverses, the core claim depends on the conductive-loss assumption. A second, independent probe: train a small classifier to predict a listener's audiogram or enhancement-system identity from the 65 dB SPL normalized audio features; if audiogram information is still recoverable, the decorrelation step has not removed the information the paper claims to exclude.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that the listener's audiogram is not the most informative input for intelligibility prediction; their own previously measured intelligibility scores are. The authors introduce SSIPNet, where a frozen Whisper v3 encoder produces per-sample embeddings that are conditioned on the known score, and a permutation-invariant aggregator (global average pooling over the support embeddings) builds a listener-level representation that is concatenated with the query embedding and linearly read out to a predicted score. Trained in listener-structured batches on the CPC dataset, with audio normalized to 65 dB SPL and ground-truth scores calibrated through the conductive-loss level-intelligibility curves of reference [22] to strip audiogram-related level information, the model reports average RMSE 23.43 versus 26.16 for the audiogram-based baseline and NCC 0.811 versus 0.794. The paper further reports that performance improves once at least four support pairs are available, saturates near sixteen, and with a single pair is already comparable to audiogram-conditioned prediction.

Load-bearing premise

The load-bearing premise is that every listener in the CPC dataset can be treated as having conductive hearing loss, so the level-intelligibility curves of reference [22] validly rescale each audio to 65 dB SPL and transform the ground-truth intelligibility scores that the model is trained and evaluated on; if the real losses are sensorineural or mixed, the calibrated scores are not true intelligibility and the reported comparison does not test real prediction.

Editorial extensions

If this is right

Hearing-aid and speech-enhancement evaluation could be personalized from a listener's existing listening history instead of requiring a clinical audiogram.
Intelligibility prediction becomes a few-shot regression problem: the same framework naturally consumes any number of support pairs, and the paper shows useful gains already at four to sixteen pairs.
A single (audio, score) support pair per listener already matches audiogram-conditioned prediction, implying minimal data collection could initialize a personalized model.
Speech foundation model embeddings carry enough phonetic, semantic, and perceptual information to serve as the substrate for listener-specific conditioning.
The conductive-loss calibration procedure, if valid, provides a reusable recipe for removing enhancement-system level effects from listener-adaptive datasets.

Reading between the lines

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

The clear next test the paper does not run is evaluating SSIPNet on raw, uncalibrated intelligibility scores from listeners with sensorineural loss, where the conductive-loss rescaling of reference [22] does not apply; if the support-pair advantage survives there, the paradigm generalizes beyond the CPC setup.
The saturation near sixteen support pairs suggests the bottleneck is the capacity of the average-pooled listener embedding, not the data; attention-based support aggregation or a larger listener embedding could push the plateau and is directly testable.
Because the CPC enhancement systems are themselves tuned to each listener's audiogram, the decorrelation step removes level but not necessarily spectral audiogram traces; an ablation that randomizes enhancement-system identity across support and query would quantify any remaining leakage.
A practical product reading implicit in the paper is that a hearing aid or companion app could accumulate (audio, intelligibility) pairs from daily use and continuously refine a listener embedding, turning the model into a lifelong personalization component.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The core idea here is genuinely new: instead of adding listener metadata, profile a listener from a handful of (audio, score) pairs and use a frozen speech encoder to generalize to unseen audio. That is a sensible direction and the architecture is a clean, honest extension of the CPC2 winner with global average pooling over support embeddings. The curve showing performance versus number of support pairs is also useful evidence that the approach saturates quickly. I think the paper is worth reading for the framing alone.

That said, the central comparison does not yet support the strong claim. Section 2.1 recalibrates the ground truth intelligibility scores using Hood and Poole level-intelligibility curves under an explicit assumption that all 27 CPC listeners have conductive hearing loss. That assumption is unverified and, for a typical hearing-impaired population, likely wrong. Worse, the recalibration formula uses each listener's audiogram (the average loss at 0.5–2 kHz) to shift the score, so the training target itself encodes audiogram information. Tables 2 and 3 then evaluate against this synthetic target, not the measured scores. The “No Audiogram” label is therefore misleading: the method still leans on audiograms through the calibration step, and if the conductive-loss assumption fails, the reported RMSE/NCC gains may be an artifact of the transformation.

Other soft spots are real but less damaging. Only 5 test listeners appear in each fold, there are no error bars or significance tests, the baseline is a re-implementation rather than the original system, and no code or splits are provided. The average improvement (RMSE 23.4 vs 26.2; NCC 0.811 vs 0.794) is consistent across folds but modest, and with N=5 listeners I would not bet on it without a paired analysis.

This is not a paper to reject for sloppiness or incoherence—the authors state their assumption plainly and the method is reproducible in principle. But as reported, the evidence does not establish that support (audio, score) pairs outperform audiogram-based prediction on real intelligibility scores. The right path is peer review with a request to either justify the conductive-loss assumption using the CPC listener demographics or, better, re-run the comparison on the raw, uncalibrated scores. If the authors can do that, the few-shot idea has real legs.

Referee Report

3 major / 5 minor

Summary. The paper proposes SSIPNet, a few-shot model for personalized speech intelligibility prediction that takes a listener's existing (audio, score) pairs as support samples and predicts scores for new audio using a Whisper-based feature extractor and a support-set aggregation module. To prevent the model from exploiting a correlation between listener audiograms and audio RMS levels in the CPC dataset, Section 2.1 normalizes audio to 65 dB SPL and recalibrates the ground-truth intelligibility scores using conductive hearing loss curves from Hood and Poole (1971). Experiments on the CPC dataset with 5 test listeners per fold report lower RMSE and higher NCC than a re-implemented audiogram-based baseline (E011), and Section 3.3 studies the effect of the number of support pairs.

Significance. The idea of personalizing intelligibility prediction from a small number of listener-specific scores is interesting and could reduce reliance on audiograms. The use of speech foundation models and support-set aggregation is a reasonable technical direction, and the paper gives a clear architectural description. However, the experimental validation is compromised by the ground-truth recalibration, which injects audiogram information into the labels, and by the small, statistically unquantified test population. The claimed advantage over audiogram-based prediction is therefore not established as a genuine property of the method.

major comments (3)

[Section 2.1, Figure 3] The calibration step is load-bearing and unjustified. The recalibrated target is S1 = S0 + CHL(L1) − CHL(L0), where HL is the listener's average hearing loss at 0.5, 1, and 2 kHz. This makes the ground-truth scores used for training and evaluation a function of the audiogram, so the 'No Audiogram' claim is not actually tested. Moreover, the assumption that all CPC listeners have conductive hearing loss is asserted without evidence; the typical hearing-impaired population is predominantly sensorineural, for which level-intelligibility curves differ qualitatively from conductive curves. If this assumption is wrong, Tables 2 and 3 measure performance on a synthetic target rather than the experimentally-acquired intelligibility scores, and the reported superiority of SSIPNet may be an artifact of the transformation.
[Section 3.2, Tables 2 and 3] The evaluation is based on only 5 test listeners per fold and no error bars or significance tests are reported. The NCC improvement is not consistent across folds: SSIPNet is worse than the baseline in fold 2 (0.787 vs 0.809). With n=3 folds, an average NCC improvement of 0.017 and an RMSE improvement of 2.73 are not shown to be statistically reliable, especially given the very small number of listeners. This weakens the central claim that support pairs outperform audiogram-based prediction.
[Section 2.1] Normalizing the RMS level of all audio to 65 dB SPL does not remove audiogram information encoded in the spectral shape, dynamic range compression, and other processing applied by enhancement systems that are personalized for each listener. The correlation shown in Figure 2 is only with RMS level, but the audio content itself may still carry audiogram-dependent cues. Therefore the 'decorrelation' is incomplete, and the model may still access audiogram-related information through the audio, confounding the interpretation that improvements come from the support (audio, score) pairs alone.

minor comments (5)

[Section 3.3, Figure 5] The text states that the best performance is achieved with 16 support samples, yet Tables 2 and 3 report results with 64 support samples. Please clarify which configuration is the main result and justify the choice; otherwise the reader may suspect selective reporting.
[Section 3.1.3, Equation (2)] The notation in Equation (2) is ambiguous: the symbol y is used for both the ground-truth value and its mean. Use \bar{y} for the mean in the Pearson correlation definition.
[Section 3.1.2] The baseline E011 is re-implemented rather than taken from the official CPC2 results, but no evidence is provided that the re-implementation faithfully reproduces the original method's performance. Please report a comparison on the original challenge test set or provide a reference implementation check.
[Section 3.1.1] The authors restructured the CPC data splits because the official test set had too few samples per listener. This is a significant deviation from the challenge protocol; please discuss more thoroughly how this affects comparability with prior published CPC results.
[References] Reference [2] lists 'J. Jensen, C. H. Taal, J. Jensen, and C. H. Taal' with repeated authors; this appears to be a formatting error and should be corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found: §2.1's audiogram-based score calibration is a validity caveat, not an input-output identity, so the paper's prediction chain is not circular.

full rationale

The derivation chain is self-contained and externally grounded. The model is trained and evaluated on the CPC dataset with a re-implemented external baseline E011 [7]; no fitted parameter is renamed as a prediction, and no load-bearing self-citation appears (reference [7] is external work whose architecture is adopted transparently). The only step that warrants scrutiny is §2.1, where the raw CPC scores are recalibrated as S1 = S0 + CHL(L1) − CHL(L0), with HL derived from the audiogram. This does inject audiogram-derived information into the label and the support scores, and the assumption that all 27 listeners have conductive hearing loss is unsupported. However, the model never receives HL as input, and the prediction is not algebraically equal to the calibration formula; the recalibration is a label transformation, not an input-output identity. Hence the 'No Audiogram' claim is weakened as a validity matter, but the method's derivation does not reduce to its own inputs by construction. No circular step meets the evidentiary bar of this review.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The central claim rests on a small set of domain assumptions, most critically the conductive hearing loss assumption used to calibrate the ground truth. No new physical or conceptual entities are introduced beyond the support-sample framing.

free parameters (2)

Calibration curves CHL(L) from Hood and Poole [22] = 65 dB SPL target level
Chosen by hand from the literature to transform ground truth scores; the central evaluation depends on this choice.
Number of support samples (64) = 64
Selected by the authors for the main result; performance varies with this number (Figure 5).

assumptions (5)

domain assumption All listeners in the CPC dataset have conductive hearing loss
Stated in Section 2.1; without this, the Hood and Poole curves cannot be applied.
domain assumption The level-intelligibility relationship from [22] applies to each listener given only average hearing loss
Used to rescale scores to 65 dB SPL in Figure 3.
domain assumption Whisper large-v3 features, pooled and transformed as in [7], are sufficient for intelligibility prediction
Borrowed from CPC2 champion [7]; not re-validated here.
standard math Global average pooling over support embeddings preserves listener-specific information
The permutation-invariant aggregation is standard, but its adequacy is empirical.
domain assumption Audio spectral content does not encode audiogram information after RMS normalization
The paper only normalizes level; enhancement systems may still imprint audiogram-related spectral patterns.

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Cite this review

Pith. "Pith review of No Audiogram: Leveraging Existing Scores for Personalized Speech Intelligibility Prediction." pith.science (2026). https://pith.science/paper/2FWMSECD

@misc{pith2026250602039,
  author       = {Pith},
  title        = {Pith review of: No Audiogram: Leveraging Existing Scores for Personalized Speech Intelligibility Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2FWMSECD}},
  note         = {Machine review of arXiv:2506.02039}
}

read the original abstract

Personalized speech intelligibility prediction is challenging. Previous approaches have mainly relied on audiograms, which are inherently limited in accuracy as they only capture a listener's hearing threshold for pure tones. Rather than incorporating additional listener features, we propose a novel approach that leverages an individual's existing intelligibility data to predict their performance on new audio. We introduce the Support Sample-Based Intelligibility Prediction Network (SSIPNet), a deep learning model that leverages speech foundation models to build a high-dimensional representation of a listener's speech recognition ability from multiple support (audio, score) pairs, enabling accurate predictions for unseen audio. Results on the Clarity Prediction Challenge dataset show that, even with a small number of support (audio, score) pairs, our method outperforms audiogram-based predictions. Our work presents a new paradigm for personalized speech intelligibility prediction.

Figures

Figures reproduced from arXiv: 2506.02039 by the authors.

**Figure 1.** shows that on the CPC dataset, individuals with similar average hearing levels can have significant differences in their speech intelligibility score, which raises questions about * Corresponding authors [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

**Figure 2.** The levels of audios played to each listener have a strong correlation with his/her audiogram on the CPC dataset. Each point represents one listener. The dotted line is the linear regression curve and r is the Pearson Correlation Coefficient. pairs into a personalized listener embedding, from which the intelligibility score is predicted. Unlike standard audiogrambased methods, SSIPNet implicitly models higher-order… view at source ↗

**Figure 4.** Our SSIPNet model structure. It follows a pipeline of sample-level embedding learning, listener-level feature aggregation, and final intelligibility score prediction. Module blocks with the same color indicate shared weights. The left side represents our speech foundation model-based feature extraction module, where the channel dimension has been omitted for simplicity. The right side illustrates our Support-Based P… view at source ↗

Figures from the paper (1 more)

**Figure 5.** Figure 5: Relationship between prediction performance and the number of support (audio, score) pairs. The dotted horizontal lines indicate the audiogram-based baseline. The numbers represent the average results across the three folds. recognition ability beyond what the audiogr…

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No Audiogram: Leveraging Existing Scores for Personalized Speech Intelligibility Prediction

Introduction Metrics are crucial for algorithm optimization. For perceptual speech algorithms, such as speech enhancement and wide dy- namic range compression, speech intelligibility score is one of the most important metrics. However, obtaining accurate speech intelligibility data is non-trivial, as it requires conduct- ing numerous listening tests with ...

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

No Audiogram: Leveraging Existing Scores for Personalized Speech Intelligibility Prediction

Introduction Metrics are crucial for algorithm optimization. For perceptual speech algorithms, such as speech enhancement and wide dy- namic range compression, speech intelligibility score is one of the most important metrics. However, obtaining accurate speech intelligibility data is non-trivial, as it requires conduct- ing numerous listening tests with ...

work page Pith review arXiv 2025

[2] [2]

Method 2.1. Audio-audiogram decorrelation We observed a strong correlation between the listener’s audio- gram and the RMS level of the audios presented to that listener on the CPC dataset, as shown in Figure 2. This correlation is expected, as the audios are generated by different enhancement systems, each aimed at providing optimal compensation for in- d...

work page

[3] [3]

Experimental setup 3.1.1

Experiments and results 3.1. Experimental setup 3.1.1. Data For our experiments, we used the Clarity Prediction Challenge (CPC) dataset [10, 11], which consists of 13126 distinct au- dio signals with experimentally-acquired intelligibility scores. These audio samples are generated by 18 enhancement systems and subsequently presented to 27 hearing-impaired...

work page

[4] [4]

Conclusion This paper introduces SSIP, a method for personalized speech intelligibility prediction that generalizes intelligibility scores from existing audio samples to unseen audio. Our experiments demonstrate that, with a carefully designed model architecture, methods leveraging a relatively small number of support (au- dio, score) pairs can outperform...

work page

[5] [5]

A short- time objective intelligibility measure for time-frequency weighted noisy speech,

C. H. Taal, R. C. Hendriks, R. Heusdens, and J. Jensen, “A short- time objective intelligibility measure for time-frequency weighted noisy speech,” in 2010 IEEE International Conference on Acous- tics, Speech and Signal Processing, 2010, pp. 4214–4217

work page 2010

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An algorithm for predicting the intelligibility of speech masked by modulated noise maskers,

J. Jensen, C. H. Taal, J. Jensen, and C. H. Taal, “An algorithm for predicting the intelligibility of speech masked by modulated noise maskers,” IEEE/ACM Trans. Audio, Speech and Lang. Proc. , vol. 24, no. 11, p. 2009–2022, Nov. 2016

work page 2009

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Mti-net: A multi-target speech intelligibility prediction model,

R. E. Zezario, S. wei Fu, F. Chen, C.-S. Fuh, H.-M. Wang, and Y . Tsao, “Mti-net: A multi-target speech intelligibility prediction model,” in Interspeech 2022, 2022, pp. 5463–5467

work page 2022

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End-to-end speech intelligibility prediction using time- domain fully convolutional neural networks,

M. B. Pedersen, M. Kolbæk, A. H. Andersen, S. H. Jensen, and J. Jensen, “End-to-end speech intelligibility prediction using time- domain fully convolutional neural networks,” inInterspeech 2020, 2020, pp. 1151–1155

work page 2020