Tackling Cognitive Impairment Detection from Speech: A submission to the PROCESS Challenge

Alberto Abad; Anna Pompili; Carlos-D. Mart\'inez-Hinarejos; Catarina Botelho; David Gimeno-G\'omez; David Martins de Matos; Diogo A.P. Nunes; Francisco Teixeira; Isabel Trancoso; John Mendon\c{c}a

REVIEW 3 major objections 4 minor 42 references

Tackling Cognitive Impairment Detection from Speech: A submission to the PROCESS Challenge

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read The paper claims that detecting cognitive impairment from spontaneous speech is best done by fusing a diverse set of acoustic and textual models—trained on three clinical tasks—into a logistic-regression ensemble, which reached 59.34%…

desk verdict Solid, honest challenge paper whose central ensemble claim is plausible but under-supported by development-set selection on a 4-subject dementia class. read the letter →

arxiv 2501.00145 v1 pith:4T3UXGUA submitted 2024-12-30 eess.AS cs.SD

Catarina Botelho , David Gimeno-Gómez , Francisco Teixeira , John Mendonça , Patrícia Pereira , Diogo A.P. Nunes , Thomas Rolland , Anna Pompili

show 6 more authors

Rubén Solera-Ureña Maria Ponte David Martins de Matos Carlos-D. Martínez-Hinarejos Isabel Trancoso Alberto Abad

This is my paper · ORCID

classification eess.AScs.SD

keywords cognitiveimpairmentdetectiondementiamildspeechbiomarkersmodelensemblemacrodescriptorspausefeatureslogisticregressionfusion

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 reports an entry in the PROCESS Challenge whose goal is three-way detection of healthy control, mild cognitive impairment, and dementia from spontaneous speech elicited by three clinical tasks. The authors' central claim is that the best systems are not any single feature set or classifier but logistic-regression ensembles that fuse complementary models, drawing on acoustic and textual information from all three tasks. Their selected six-model ensemble reached 59.34% macro F1 on the official test set, up from 57.3% for the best single system on development data. The work matters because it shows that diverse speech and language representations—including pause timing, fluency statistics, LLM-derived macrodescriptors, and neural embeddings—can be combined to screen for cognitive decline in a realistic three-class, imbalanced setting.

What carries the argument

The central object is the model-ensemble pipeline built from a pool of 205 single systems. After selecting 15 candidates that balance cross-validation and development macro F1 while keeping a non-zero dementia-class F1, the authors exhaustively combine subsets of two to six systems, aggregating their soft outputs (probabilities, softmax scores, or SVM distances to hyperplanes) with a multinomial logistic regression trained on cross-validation predictions. The load-bearing identity is complementarity: systems that are individually mediocre but use different representations and tasks contribute non-redundant information, and the ensemble's selection criterion is deliberately tuned to avoid the common failure of ignoring the under-represented dementia class.

What would settle it

Take the official training and development partitions and repeatedly re-partition them into many train/dev splits with at least ten dementia speakers in the dev fold, then run the same ten-thousand-ensemble selection on each split. If the selected ensembles' macro F1 and dementia F1 fluctuate widely across splits, or if a similarly selected ensemble fails to beat single systems on an external dementia-speech corpus, the paper's complementarity claim would be falsified.

Watch

Extended reading notes

Core claim

On the authors' own terms, the paper establishes that model fusion through a multinomial logistic regression over soft class scores substantially outperforms individual systems for three-class cognitive impairment detection. Among 205 single systems, the reliable contributors turned out to be complementary rather than individually strongest: an SVM using pause features and macrodescriptors on phonemic fluency, the same feature pair on semantic fluency, a fine-tuned Longformer on the Cookie Theft description, and speaker-level ECAPA-TDNN and ECAPA+TRILLsson embeddings pooled across all three tasks. The two final ensembles share five of six systems and reach 67.8% and 67.4% macro F1 on development, with official-test macro F1 of 54.36% and 59.34% respectively.

Load-bearing premise

The whole result rests on the assumption that the held-out development set—whose dementia class has only four subjects—is representative enough to pick the best of about ten thousand ensemble combinations, so that the development-based choice generalizes to the official test.

Editorial extensions

If this is right

Fusing diverse modalities and task-specific models beats any single representation, so future systems should focus on complementarity rather than on chasing one strong feature set.
Knowledge-based features such as pause timing, fluency counts, and LLM macrodescriptors remain highly informative alongside neural embeddings, suggesting that cheap interpretable features can anchor robust ensembles.
The dementia class is the bottleneck: selection rules that require a non-zero dementia F1 are necessary, and real gains depend on better dementia-specific signal rather than overall accuracy.
Transcript quality from automatic speech recognition differs by task, with fluency tasks being the hardest, so task-specific ASR choices are part of the winning recipe.

Reading between the lines

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

A direct testable extension is to add demographic variables such as age, sex, and education to the fusion stage; the paper omits them for data reasons, but a physician-facing tool would likely gain from them.
Because the development set's dementia class contains only four subjects, the ensemble-selection criterion is statistically unstable; re-running the selection on stratified or larger development partitions would reveal how much of the reported gain is selection luck.
The same fusion recipe could transfer to other speech-based clinical screening tasks with a small, imbalanced target class, provided the individual classifiers produce compatible soft-output scores.

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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

Quick take: this is a solid, honest challenge-submission paper, but the load-bearing claim—that heterogeneous ensembles beat single systems—rests on development-set comparisons made after searching ~10k combinations, and the development partition has only 4 dementia subjects. The official-test numbers are consistent with the claim but don't establish it, because only two ensembles were scored and no single-system test scores are reported.

What's actually new: the empirical result on the PROCESS three-class task, specifically the fusion of pause features, LLM macrodescriptors, and neural embeddings, plus the selection analysis showing which component systems recur in good ensembles. That kind of post-hoc descriptive analysis is useful for the challenge community. The paper is also unusually honest—the limitations section flags overfitting risk, class imbalance, and interviewer confounds, and doesn't oversell clinical readiness. The protocol is standard: five-fold CV, held-out dev, separate test, with a clear description of the ASR preprocessing choice.

The soft spots are real but proportionate. The final ensembles were picked from roughly 10k combinations by balancing train/dev macro F1 and dementia F1, and the dev set has just 4 dementia subjects. That makes the F1_Dem component of the selection criterion very coarse and noisy. The instability shows on the test set: Ensembles #1 and #2 share 5 of 6 component systems yet land 4.98 points apart (54.36 vs 59.34). A more careful analysis would report per-system test performance, error bars or bootstrap intervals, and ideally a nested or pre-registered selection to avoid the multiple-comparisons issue. The paper's own limitations section already concedes the overfitting risk, and the reader's conditional verdict is on target. The absence of code and data is a minor additional drag, though typical for challenge papers.

Bottom line: this is a benchmark paper, not a new method, and the absolute numbers are modest (59.3% macro F1 on a three-class task). But it's a fair and useful contribution for people working on speech-based cognitive impairment screening, and the critique we'd want from a referee is not that the idea is wrong but that the evidence strength is overstated. I'd send it to review—it deserves referee time—and expect a major/minor revision that tightens the statistical claims. I'd bring it to a reading group, mostly to discuss the model-selection problem, which is a good teaching moment.

Referee Report

3 major / 4 minor

Summary. This paper describes the authors' submission to the PROCESS 2024 challenge, a three-class (Healthy Control, MCI, Dementia) speech-based cognitive impairment detection task. The authors compare a wide range of acoustic, linguistic, and neural feature sets (eGeMAPS, Praat, pauses, macrodescriptors, ECAPA-TDNN, TRILLsson, Longformer, BERT/RoBERTa, etc.) with several classifiers, then fuse the most promising 15 single systems via multinomial logistic regression, selecting two final ensembles from roughly 10,000 combinations using cross-validation and development-set macro F1 and dementia-class F1. The paper reports that the best ensemble reaches 59.34% macro F1 on the official test set, up from 57.3% for the best single system on development, and argues that the selected ensembles combine complementary acoustic and textual information from all three clinical tasks.

Significance. If the central claim holds, this is a useful empirical contribution to the PROCESS challenge benchmark, documenting a broad feature/classifier comparison and a practical fusion strategy for a highly imbalanced three-class clinical task. The paper's strengths include a comprehensive exploration of diverse representations, careful ASR model selection (including disfluency-oriented transcription), and a transparent description of the ensemble selection procedure. However, the reported advantage of ensembles over single systems is not supported by official-test evidence because only two ensembles were scored on the test set, and the development set used for selection is very small, especially for the dementia class. The authors themselves acknowledge the overfitting risk from extensive comparison on a limited dataset (Section 5).

major comments (3)

[Section 3.4 and Table 4] The central claim that ensembles outperform single systems is based on development-set comparisons made after selecting the final ensembles from approximately 10,000 combinations. The development partition contains only 4 dementia subjects (Table 1), so the F1_Dem component of the selection criterion (57.2% and 57.1% for Ens#1 and Ens#2) is extremely noisy. This instability is visible in the official-test results: Ens#1 and Ens#2 share 5 of 6 component systems (Table 3) yet differ by 4.98 points on the official test (54.36% vs. 59.34%), which is implausibly large for two near-identical ensembles if the selection process was stable. To support the complementarity claim, the authors should report official-test macro F1 (and class-wise F1) for all single systems and for a larger set of ensembles, along with confidence intervals or significance tests.
[Section 5, Limitations (paragraph 4)] The paper concedes that "the risk of overfitting increases due to the extensive comparison of approaches on a limited dataset," but it does not quantify this risk in relation to the selection procedure. With 205 single systems and roughly 10,000 ensemble combinations examined, the effective number of statistical comparisons is large relative to the 42 development subjects (4 dementia). The conclusion that "model fusion through logistic regression substantially improves performance" (Section 4) is therefore not established by the presented evidence. The authors should provide a correction or sensitivity analysis (e.g., repeated random splits, nested cross-validation, or reporting of the full distribution of ensemble performances on development) to show that the selected ensembles' advantage is not attributable to selection bias.
[Section 4, Results & Discussion] The paper reports official-test performance only for the two final ensembles (54.36% and 59.34%), not for the 15 constituent single systems or for any intermediate ensembles. Without per-system test scores, the reader cannot verify the claimed improvement of ensembles over single systems on held-out test data. The 57.3% figure cited as the best single system is from the development set, not the test set, and the development set was used for selection, so this is not an unbiased comparison. Reporting test-set results for all single systems and for the ensembles that were actually evaluated would make the claims testable.

minor comments (4)

[General] The paper would benefit from a proofread for minor language issues, e.g., "real-word" in Section 5 should be "real-world," and "reducted" in Table 3 should be "reduced."
[Table 2] The ASR WER results are informative, but the hallucination issue in fluency tasks is mentioned only briefly; a short example of a hallucinated transcription would clarify the severity of the problem.
[Figure 4] The color coding of the figure (especially the yellow points for zero dementia F1) is not described in the caption; adding a legend or explicit color labels would improve readability.
[Section 3.4] The sentence "the risk of overfitting increases due to the extensive comparison of approaches on a limited dataset" appears in Section 5, but it would be more transparent to also mention this risk at the point where the 10k ensemble search is described.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: official held-out test results anchor the empirical claims, and self-citations are feature definitions rather than load-bearing evidence.

full rationale

This is a shared-task system description, not a derivation. The headline test-set macro F1 values (54.36% and 59.34%) come from the official held-out test set, which is external to the ensemble selection performed on development data. The ensemble selection criterion in Section 3.4 (balancing train/development macro F1 and dementia-class F1) is a model selection procedure, not a quantity defined in terms of the reported result. Self-citations to [25] and [28] are used only to name feature extractors, and the paper evaluates those features on this corpus rather than assuming their effectiveness from the citations. The reported instability between Ensemble #1 and Ensemble #2 on the official test, and the Section 5 warning about overfitting from extensive comparison on a limited dataset, are generalization concerns, not circularity. No load-bearing step reduces by construction or by self-citation to its own inputs.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim is an empirical performance claim, so the ledger mostly records engineering choices and domain assumptions rather than derivation inputs.

free parameters (4)

SVM regularization C for fluency features = 0.1
Used for systems 2, 3, 4; chosen by hand based on preliminary experiments (Section 3.3).
SVM regularization C for ECAPA embeddings = 0.0001
Used for systems 6 and 7 (Table 3).
Number of ensemble systems = 6 (out of up to 6)
Ensembles of up to six single systems were explored; final ensembles use six (Section 3.4).
Selection threshold for promising ensembles = macro F1 > 55% and non-zero dementia F1
Used to define the 421 top-performing ensembles for fine-grained analysis (Section 4).

assumptions (4)

domain assumption The PROCESS challenge diagnostic labels (HC, MCI, Dementia) are clinically accurate.
The paper relies on these labels as ground truth for training and evaluation (Section 2).
domain assumption Automatic transcripts from Whisper-medium.en and CrisperWhisper are adequate substitutes for manual transcripts.
All text-based features and models use ASR output rather than manual transcripts (Section 3.1).
domain assumption Pre-trained neural models (Longformer, ECAPA-TDNN, TRILLsson) carry task-relevant information when fine-tuned or used as feature extractors on this small clinical dataset.
The approach assumes transfer from large pre-trained models to the PROCESS data (Section 3.2).
domain assumption The development set is representative of the test set distribution, including age and class imbalance.
Final model selection relies on dev performance (Section 3.4, Table 4).

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Cite this review

Pith. "Pith review of Tackling Cognitive Impairment Detection from Speech: A submission to the PROCESS Challenge." pith.science (2026). https://pith.science/paper/4T3UXGUA

@misc{pith2026250100145,
  author       = {Pith},
  title        = {Pith review of: Tackling Cognitive Impairment Detection from Speech: A submission to the PROCESS Challenge},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4T3UXGUA}},
  note         = {Machine review of arXiv:2501.00145}
}

read the original abstract

This work describes our group's submission to the PROCESS Challenge 2024, with the goal of assessing cognitive decline through spontaneous speech, using three guided clinical tasks. This joint effort followed a holistic approach, encompassing both knowledge-based acoustic and text-based feature sets, as well as LLM-based macrolinguistic descriptors, pause-based acoustic biomarkers, and multiple neural representations (e.g., LongFormer, ECAPA-TDNN, and Trillson embeddings). Combining these feature sets with different classifiers resulted in a large pool of models, from which we selected those that provided the best balance between train, development, and individual class performance. Our results show that our best performing systems correspond to combinations of models that are complementary to each other, relying on acoustic and textual information from all three clinical tasks.

Figures

Figures reproduced from arXiv: 2501.00145 by the authors.

**Figure 1.** Overall schema of our proposed method. This study is the result of a collaboration between researchers with diverse expertise in speech and language with the goal of participating in the PROCESS challenge 2024. Therefore, it compares very diverse approaches for the three-class classification task, exploring various data representations, including knowledge-based (KB) acoustic and linguistic features, text- and sp… view at source ↗

**Figure 2.** Distribution of the MMSE scores per diagnostic class. 3.1. Pre-processing An initial manual analysis of the recordings identified two possible confounding factors: the presence of loud noises and the interventions of interviewers. While the former was dealt with through signal processing approaches, for the latter, we experimented with speaker diarization systems for the automatic removal of the interventions. Ho… view at source ↗

**Figure 4.** In this figure, it is possible to observe that a large num [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗

Figures from the paper (2 more)

**Figure 3.** Figure 3: Single system performance, in terms of macro F1 (a) and F1 on the dementia class (b) [PITH_FULL_IMAGE:figures/full_fig_p004_3.png]

**Figure 4.** Figure 4: Model ensemble performance, in terms of macro F1. in the training and development sets and a non-zero F1 score in the dementia class [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]

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Reference graph

Works this paper leans on

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It is marked by a progressive decline in cognitive functions beyond what is considered normal in bio- logical aging [1]

Introduction Dementia, which affects fifty-five million people around the world, is one of the major causes of disability and dependency among older people [1]. It is marked by a progressive decline in cognitive functions beyond what is considered normal in bio- logical aging [1]. Alzheimer’s disease (AD), a progressive neu- rodegenerative disorder, is th...

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Tackling Cognitive Impairment Detection from Speech: A submission to the PROCESS Challenge

Corpus The PROCESS Challenge dataset [17] is designed to promote research on early detection of dementia through speech analy- sis, emphasizing practical applications in real-world scenarios. Dataset Overview. The corpus includes three diagnostic classes, representing a scenario for diagnosing early-stage de- mentia. The Healthy Control (HC) group compris...

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Method This study evaluates various systems for distinguishing healthy controls, from individuals with MCI and individuals with De- mentia, based on their speech recorded for the three tasks de- tailed in Section 2. The compared systems target the multi- ple individual manifestations of early onset of cognitive impair- ment, namely those observed in speec...

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The results for the individual sys- tems are represented in Figure 3, where the best-performing models can be found in the top right corner

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A second limitation is that our approach does not consider demographic data, which physicians typically consider in clini- cal scenarios

Limitations Among the limitations of this work, we first highlight the chal- lenges arising from the dataset, including class imbalance and overlapping MMSE scores between classes – factors which may partly explain the poorer performance compared to prior studies on ADReSS [34, 28] or Taukadial [9] challenges, even consid- ering that those were two-class ...

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We conducted extensive experimentation, addressing the challenge from multiple perspectives

Conclusions This paper presents our joint group efforts for the PROCESS challenge [17], which aims to assess cognitive decline through the automatic analysis of spontaneous speech production. We conducted extensive experimentation, addressing the challenge from multiple perspectives. This included evaluating the accu- racy of various ASR models, exploring...

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

It is marked by a progressive decline in cognitive functions beyond what is considered normal in bio- logical aging [1]

Introduction Dementia, which affects fifty-five million people around the world, is one of the major causes of disability and dependency among older people [1]. It is marked by a progressive decline in cognitive functions beyond what is considered normal in bio- logical aging [1]. Alzheimer’s disease (AD), a progressive neu- rodegenerative disorder, is th...

work page 2024

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Tackling Cognitive Impairment Detection from Speech: A submission to the PROCESS Challenge

Corpus The PROCESS Challenge dataset [17] is designed to promote research on early detection of dementia through speech analy- sis, emphasizing practical applications in real-world scenarios. Dataset Overview. The corpus includes three diagnostic classes, representing a scenario for diagnosing early-stage de- mentia. The Healthy Control (HC) group compris...

work page Pith review arXiv 2024

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Method This study evaluates various systems for distinguishing healthy controls, from individuals with MCI and individuals with De- mentia, based on their speech recorded for the three tasks de- tailed in Section 2. The compared systems target the multi- ple individual manifestations of early onset of cognitive impair- ment, namely those observed in speec...

work page

[4] [4]

The results for the individual sys- tems are represented in Figure 3, where the best-performing models can be found in the top right corner

Results & Discussion Single-system experiments. The results for the individual sys- tems are represented in Figure 3, where the best-performing models can be found in the top right corner. In this figure, we can observe how systems trained on the PFT task – denoted by a ⋄ – generally achieve the highest performance. Moreover, KB features and their combina...

work page

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A second limitation is that our approach does not consider demographic data, which physicians typically consider in clini- cal scenarios

Limitations Among the limitations of this work, we first highlight the chal- lenges arising from the dataset, including class imbalance and overlapping MMSE scores between classes – factors which may partly explain the poorer performance compared to prior studies on ADReSS [34, 28] or Taukadial [9] challenges, even consid- ering that those were two-class ...

work page

[6] [6]

We conducted extensive experimentation, addressing the challenge from multiple perspectives

Conclusions This paper presents our joint group efforts for the PROCESS challenge [17], which aims to assess cognitive decline through the automatic analysis of spontaneous speech production. We conducted extensive experimentation, addressing the challenge from multiple perspectives. This included evaluating the accu- racy of various ASR models, exploring...

work page

[7] [7]

A way of making Europe

Acknowledgements The work of Gimeno-G ´omez and Mart´ınez-Hinarejos was par- tially supported by GV A through Grants CIACIF/2021/295 and CIBEFP/2023/167, by Grant PID2021-124719OB-I00 un- der project LLEER funded by MCIN/AEI and ERDF, EU “A way of making Europe”. The work of the remain- ing authors was supported by Portuguese national funds through Fundac...

doi:10.54499/uidb/50021/2020 2021

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Dementia,

W. H. Organization, “Dementia,” https://www.who.int/ news-room/fact-sheets/detail/dementia, accessed on September 28, 2021

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