-->PONE-D-25-44894-->-->Prediction of Cognitive Impairment through Speech Data Analysis: A Comparative Evaluation of Deep Learning Models-->-->PLOS One

Dear Dr. Youn,

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Dear authors,

Thank you for your submission. At this moment, we couldn't accept your article. Would you please consider below comments, revise it and submit for further review?

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Farman Ullah

Academic Editor

PLOS One

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Additional Editor Comments:

Dear authors,

Thank you for your submission. At this moment, we couldn't accept your article. Would you please consider below comments, revise it and submit for further review?

-->--> -->-->[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions-->

-->Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #1: Yes

Reviewer #2: Yes

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-->2. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: No

Reviewer #2: Yes

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-->3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

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-->4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.-->

Reviewer #1: No

Reviewer #2: Yes

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-->5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1: This manuscript explores an important and emerging field of speech-based detection of cognitive impairment using deep learning models. The comparative approach across multiple architectures (1D CNN, AST, Wav2Vec 2.0, etc.) is valuable and well-aligned with the journal’s scope. However, the study requires major revisions to improve methodological transparency, statistical rigor, interpretability, and presentation clarity before it can be considered for publication.

1. Clarify dataset composition, total participants, and inclusion of all 11 speech tasks.

2. Address data imbalance (e.g., female-majority dataset) and its effect on generalization.

3. Describe preprocessing steps such as noise reduction, silence trimming, and normalization.

4. Specify details of data splits and confirm participant-level separation in cross-validation.

5. Provide hyperparameter tuning details (learning rate, epochs, optimizer, batch size).

6. Add statistical tests to verify significance of model performance differences.

7. Present confusion matrices or performance plots for key models.

8. Compare with simpler baseline models (e.g., SVM or logistic regression with MFCC).

9. Expand limitations regarding dataset size, gender bias, and language specificity.

10. Add DOI: 10.1109/OJCS.2025.3580570, this study’s use of 1D-to-2D transformations (CWT, STFT) and CNN-based modelling parallels the present work’s spectrogram approach, reinforcing its methodological validity and connecting speech-based cognitive analysis to broader applications in noninvasively biomedical signal processing.

12. Add model complexity metrics (parameters, training time, memory).

13. Strengthen abstract by summarizing dataset size and highlighting key contributions.

14. Improve figure captions and ensure consistent terminology across sections.

15. Edit text for conciseness, tense consistency, and readability for non-technical readers.

Reviewer #2: This study addresses the critical need for no-invasive diagnostic tools for cognitive decline, by evaluating multiple deep learning architectures applied to dataset from “Cognitive Impairment Diagnosis Voice/Conversation”. Using this dataset the study compares the efficacy of 1D CNN, AST and Wav2Vec2.0 in classifying binary tasks: normal cognition (NC) vs. mild cognitive impairment (MCI) and NC vs. Alzheimer’s disease (AD). The paper concludes that Wav2Vec2.0 outperforms other architectures, particularly in the NC vs. AD classification task.

The primary strength of this work lies in systematic, side by side evaluation of disparate modeling approaches (eg. VGG-19, ResNet, transformer-based models) to audio models providing a clear hierarchy of effectiveness. In particular, the results reveal that self-supervised speech representation model such as Wav2Vec2.0 is better suited for cognitive changes in speech.

I had a few comments that I believe would improve the paper and its applicability.

1.This study prioritizes female voices due to lower volume of male recordings. Although this choice is understandable, it raises concern about generalizability of the results. It would have been interesting to see how sex-specific vocal characteristics might influence model performance and whether conclusions are expected to hold for equally mixed/male-dominated cohorts.

2.If I could make out correctly, the results aggregate the performance across the 11 different tasks (eg. Sentence repetition, language fluency). In the limiting case what would be the effect of analyzing these individually? Some tasks (eg. storytelling) are biologically more relevant for cognitive decline than other (Image description). I was wondering which specific tasks account for higher Wav2Vec2.0. This in turn could streamline future data collections.

3.Finally, the model performance is reported using across five-fold cross-validation. Given relatively modest performance differences between some models, reporting statistical significance testing/confidence interval would help clarify the robustness.

Overall, this manuscript presents a well-executed study using speech dataset and deep learning techniques. The comparative analysis is valuable contribution to the field of digital biomarker. I believe addressing above points would strengthen the paper.

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Reviewer #1: No

Reviewer #2: No

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Attachments

Attachment

Submitted filename: Review_PONE-D-25-44894.docx

Prediction of cognitive impairment through speech data analysis: A comparative evaluation of deep learning models

PONE-D-25-44894R1

Dear Dr. Youn,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Farman Ullah

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.-->

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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-->2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #1: Yes

Reviewer #2: Yes

**********

-->3. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: Yes

Reviewer #2: Yes

**********

-->4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

**********

-->5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.-->

Reviewer #1: Yes

Reviewer #2: Yes

**********

-->6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1: The authors have undertaken the substantial revision and the manuscript has improved considerably from its original version.

Reviewer #2: (No Response)

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-->7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.-->

Reviewer #1: No

Reviewer #2: No

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Attachments

Attachment

Submitted filename: Revised_decision.pdf