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The authors need to address the comments raised by the two reviewers.

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

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: The study is strong and clearly of publishable quality. The experimental design is sound, the analyses are thorough, and the work makes a valuable contribution to the intelligibility modeling literature. That said, several conceptual and interpretive claims would benefit from clarification or more cautious wording to avoid potential misinterpretation by readers. I view these points as refinements rather than fundamental concerns, and I believe they can be addressed without major changes to the study. I look forward to following this line of work as it continues to develop. Please see attached comments.

Reviewer #2: This study evaluates whether spectral or spectro-temporal acoustic information is required to predict speech intelligibility in Mandarin Chinese. Two models were compared: an ASR-based FADE model implemented with either spectral or spectro-temporal features, and a speech intelligibility index (SII) model relying solely on spectral information. Model predictions of absolute speech recognition thresholds (SRTs) and Lombard gain (LG) were compared with previously collected behavioral data. Results showed that LG could be predicted accurately using spectral features alone, whereas accurate prediction of absolute SRT required spectro-temporal features in the ASR model. This indicated that temporal cues are important for modeling absolute intelligibility but not relative Lombard benefit in Mandarin.

The manuscript is clearly written and technically sound. The proposed ASR-based approach has the potential to provide a more flexible and accurate estimate of intelligibility than the traditional SII model. However, I recommend a revision to clarify some key terms and concepts before the manuscript is suitable for publication.

My major comment concerns what constitutes “temporal” properties in this study. The motivation for examining Mandarin is that tonal patterns are described as having an inherently spectro-temporal structure that may make temporal dynamics more important for intelligibility (p. 2, lines 38-40). However, this appears to imply that non-tonal languages such as English or German contain less temporal structure. I don’t think this assumption is well justified. Non-tonal languages also rely heavily on time-varying acoustic cues, including dynamic formant transitions for segmental identity and F0 contours for prosodic categories (e.g., pitch accents) crucial for intonational meaning. It would therefore be helpful for the authors to clarify in what sense tonal languages are expected to be “more temporal” than non-tonal languages, or to provide supporting evidence for this distinction.

Relatedly, I am also puzzled as to why the log-mel spectrogram features in the FADE frontend are described as “spectral-only.” A spectrogram has multiple time frames and therefore preserves temporal variation across frames. It appears that, in this manuscript, “temporal” specifically refers to explicit spectro-temporal modulation patterns captured by the separable Gabor filterbank representation rather than temporal variation per se. This should be stated explicitly, and the authors should provide a clearer definition of what they mean by “temporal” features throughout the manuscript.

Given the ambiguity in how temporal information is defined here, the authors should be more cautious in suggesting that “temporal” cues contribute more strongly to speech intelligibility in tonal languages when motivating the study. In fact, I think the present findings (e.g., FADE model with SGBFB best predicted absolute SRT) may generalize to non-tonal languages as well, although, as noted in the General Remarks, this would need to be empirically tested.

Another minor question concerns the frequency range represented in the FADE and SII models. The use of log-mel spectrograms with a 16 kHz sampling rate (line 156) implies that all feature representations are limited to frequencies below 8 kHz. Indeed, the SII model assigns low importance to frequencies above 8 kHz, but there is accumulating evidence suggests that extended high-frequency information (>8 kHz) can improve speech recognition performance in both (Polspoel et al., 2022; Motlagh Zadeh et al., 2019; Monson et al., 2019) and ASR (Guo & Chandrasekaran, 2025). The authors may wish to comment on this limitation and discuss whether incorporating higher-frequency information could further improve the predictive accuracy of the FADE model.

Other comments:

1. The χ²/ν metric in the abstract is difficult to interpret without context. Consider either removing it or briefly explaining its meaning.

2. P. 7, lines 185-187: “The underlying Markov model assumes eight consecutive states per word (three representing silence and six states representing…).” Is “eight” a typo? The numbers numbers appear inconsistent (3 + 6 is not 8).

3. Formula (1) on p. 9: Should the notation use absolute value bars (| |) rather than brackets [ ], as the goal here was to find the SNR that minimized the difference between SII and SII_ref?

References:

Polspoel, S., Kramer, S. E., van Dijk, B., & Smits, C. (2022). The importance of extended high-frequency speech information in the recognition of digits, words, and sentences in quiet and noise. Ear and hearing, 43(3), 913-920.

Motlagh Zadeh, L., Silbert, N. H., Sternasty, K., Swanepoel, D. W., Hunter, L. L., & Moore, D. R. (2019). Extended high-frequency hearing enhances speech perception in noise. Proceedings of the National Academy of Sciences, 116(47), 23753-23759.

Monson, B. B., Rock, J., Schulz, A., Hoffman, E., & Buss, E. (2019). Ecological cocktail party listening reveals the utility of extended high-frequency hearing. Hearing Research, 381, 107773.

Guo, Z. C., & Chandrasekaran, B. (2025). Extended high frequencies improve phoneme recognition: Evidence from automatic speech recognition in spatial speech mixtures. The Journal of the Acoustical Society of America, 158(4), 3365-3377.

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Attachments

Attachment

Submitted filename: PONE_comments.pdf

Dear Dr. Scharf,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jul 03 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

• A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

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We look forward to receiving your revised manuscript.

Kind regards,

Jie Wang, Ph.D.

Academic Editor

PLOS One

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The authors need to address the comments of the two reviewers.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Thank you for addressing the previous comments. I believe the manuscript has improved substantially. I have a few remaining concerns that would benefit from additional clarification.

The proposed explanation that improved LG prediction accuracy arises from cancellation of a consistent bias between plain and Lombard SRT predictions is plausible, but it is currently not directly supported by evidence. Please clarify whether this bias has been empirically observed (e.g., via similar residual structure or error correlation across conditions), or alternatively frame this explanation more explicitly as a hypothesis or post hoc interpretation.

In addition, the robustness analysis excluding speakers 10 and 11 is currently described only qualitatively. Please clarify how these speakers were evaluated as non-outliers, specify the analysis performed, and provide quantitative evidence that the principal conclusions and model statistics remain stable when these speakers are excluded. Consider including formal influence diagnostics (e.g., Cook’s distance or leverage) and/or a quantitative sensitivity analysis excluding these participants.

Reviewer #2: Thank you for the revision. Most of my concerns regarding temporal information have been addressed and it’s clearer what it means in the context of this manuscript. Given the more restricted definition of temporal information now, the research question is more like “whether explicitly encoding spectral-temporal modulation patterns (within each time frame) improves prediction of LG and SRT, compared to approaches that don’t.” The authors have clarified this in the methods (3.2.1) but I think the aim of the study (2.3) should also be revised accordingly to reflect this. The key thing is to briefly explain why FADE with SGBFB features and with log-Mel spectrograms represent spectrotemporal vs. spectral-only approaches, so that readers would not feel confused later by why log-Mel spectrograms are considered to lack temporal information.

Minor comments:

1. Section 2.1. Explain what α-ratio is.

2. P. 7, line 172: “temporal informatio” --> “information”

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Spectro-temporal vs. Spectral Features to Predict the Lombard Gain in Mandarin Chinese

PONE-D-26-04256R2

Dear Dr. Scharf,

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.

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Kind regards,

Jie Wang, Ph.D.

Academic Editor

PLOS One

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Reviewers' comments:

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Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: (No Response)

Reviewer #2: The authors have satisfactorily addressed my previous comments. I believe that the manuscript is now suitable for publication.

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