-->PONE-D-25-37321-->-->Multimodal Emotion Recognition via Adaptive High-order Transformer Network-->-->PLOS ONE

Dear Dr. Lu,

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Shuai Liu

Academic Editor

PLOS ONE

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[This research was supported by the Hubei Provincial Education Science Planning Key Project (grant number 2024GA085) and 2023 Wuhan College Research Fund Program Class A Project (grant number JJA202303).]

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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: Yes

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: Yes

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: In Figure 1, Xv,XL, and XA respectively represent the inputs of video, text, and audio? It would be best if there had a brief description to facilitate reader’ reading. What is the abbreviation of BN? Is BN the BiLSTM Network?

What are the benefits of using Conv1D in the Adaptive High-order Transformer Network designed in this paper? Especially when compared with the multi-scale convolution network commonly used in current image feature extraction.

In the experimental comparison section, it is necessary to briefly explain why only the accuracy rate and the F1 score were selected for the comparison and analysis, because there are other index parameters for the evaluation of deep learning algorithms. For examples�the precision rate, the specificity and the ROC curve.

Similarly, in the ablation experiment and parameter analysis sections, it is necessary to explain why only the F1 score was selected for the comparison and analysis.

Reviewer #2: The paper introduces a combination of adaptive attention head/layer selection and high-order sparse feature learning for multimodal emotion recognition. The proposed method is evaluated on IEMOCAP and CMU-MOSEI datasets, achieving competitive performance. The work is clearly structured and includes comparisons and ablation studies. However, there are still issues related to terminology consistency, result presentation, reproducibility details, and efficiency evidence. These are mostly minor and can be resolved with targeted revisions.

1.Terminology and notation consistency

The term “Cross-Modal Feature Fusion” is abbreviated inconsistently: the abstract uses “MCFF,” while the method section and figure captions use “CMFF.” Please unify the abbreviation throughout the paper and provide a table of abbreviations when they first appear. In the ablation study, “ATS” is used, but it should be “AST” (Adaptive Selection Transformer). Please unify this throughout the text and figure captions. In Figure 1, “Corss-Modal” should be corrected to “Cross-Modal.” Please check for consistent spelling across the paper. Equation (6) uses “MHS” but this should be “MHSA” (multi-head self-attention). Also, the temperature τ used in the selection mechanism is introduced but its range and default setting are missing. Suggestion: Add a short subsection “Notation & Abbreviations” at the beginning of the Method section with a clear abbreviation table. Correct figure captions and unify terminology. Provide a symbol table and default hyperparameters in the Appendix.

2.Inconsistencies in result reporting

On IEMOCAP, Table 1 shows category-wise accuracy (e.g., Neutral accuracy 72%, F1 71.5%), but the text discussing Figure 5 states the four-class accuracies are 88/86/87/87%. Please clarify whether these come from different splits, thresholds, or metrics, and unify the reporting. In Figure 6, the caption refers to “CMU-MOSI,” while elsewhere the dataset is called “CMU-MOSEI.” Please unify the dataset name. In Table 1, the column for Graph-MFN contains “–” for all entries. Please either provide the results for this baseline or explain in a footnote why they are unavailable/unreported.

3.Efficiency evidence is insufficient

The paper claims that adaptive head/layer selection reduces redundancy and improves efficiency, but no evidence is provided in terms of FLOPs, latency, parameter counts, or energy. At least on CMU-MOSEI, please report training/inference time, parameter counts, FLOPs, and accuracy-efficiency trade-offs compared with the non-adaptive version.

4.Expression and formatting issues

Method names: the abstract uses “adaptive head selection Transformer blocks (AST) and cross-modal feature fusion blocks (MCFF),” while the Method section titles use slightly different forms. Please unify capitalization and plurality.

References: items #15 and #41 are duplicates (“Deep emotional arousal network,” Information Fusion 2022). Please remove duplicates and recheck numbering.

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Reviewer #1: No

Reviewer #2: No

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step.-->

Multimodal Emotion Recognition via Adaptive High-order Transformer Network

PONE-D-25-37321R1

Dear Dr. Lu,

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,

Shuai Liu

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewer #1:

Reviewer #2:

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: (No Response)

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: (No Response)

Reviewer #2: Yes

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

Reviewer #1: (No Response)

Reviewer #2: Yes

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-->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: (No Response)

Reviewer #2: (No Response)

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-->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: (No Response)

Reviewer #2: Yes

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-->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: (No Response)

Reviewer #2: The authors have substantially revised the manuscript in response to the reviewers’ comments. They clarified the architecture, corrected terminology and spelling errors, added explanations for symbols, introduced abbreviation and hyperparameter tables, and included additional efficiency experiments (FLOPs, parameter counts, inference time). The overall readability, consistency, and completeness of the paper have improved significantly.

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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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