PONE-D-24-21463Development and validation of a self-administered computerized cognitive assessment based on automatic speech recognitionPLOS ONE

Dear Dr. Oh,

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.

ACADEMIC EDITOR: The main issues raised include the need for clarity on ASR use in MCI populations and how speech parameters (e.g., speech rate, pauses) are handled. The term "higher cognitive functions" requires definition, and justifications for the CogMo scoring system need to be evidence-based. The methodology should detail how participants' vision and hearing were assessed, explain speech-to-text algorithms, and include a flowchart for ASR processing. The choice of MMSE over MOCA must be justified, and statistical assumptions (e.g., normality) and age differences between groups should be addressed. Clarification on handling missing values and the total ASR score calculation is needed. Finally, the diagnostic accuracy of CogMo, its ability to monitor multi-domain functions, and its advantages over traditional tests like MMSE require further justification. Please also ensure that you elaborate on your Data availability statement. 

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Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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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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Reviewer #1: Yes

Reviewer #2: No

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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 this study, the authors aimed to develop the CogMo and to investigate the psychometric properties of this computerized test in 100 community dwelling older adults with and without cognitive impairment in South Korea.

The main techniques for collecting data and processing parameters are the ASR and manual scoring, however, the underlying concepts of the CogMo and the ASR are skeptical but susceptible of improvement. The main results are generally interesting and useful. The main contribution to filling theoretical and practical gaps is clear. Nonetheless, the unique and important contributions of the current study to relevant international circles are unclear. Accordingly, I have major concerns on justifications and clarity for research instruments, statistical techniques, and generalizability of the manuscript in its present form.

Introduction

- It is evident that the ASR can be utilised to MCI in pervious studies (e.g., Tóth et al., 2018; Vincze et al., 2022; Kantithammakorn et al., 2022). The authors should make more an effort to incorporate the ASR in the MCI population.

- p.88. The authors should clarify what do they mean by 'higher cognitive functions'? since it should be linked with the structure of the CogMo.

- It is obvious from the title that the new computerized test is based on the ASR, thus this technique should be introduced to the reader and also justify the practicality of the technique.

Method

- How did the authors check the normal vision and hearing of the participants?

- The authors should explain in detail concerning the speech-to-text algorithms. It is possible that when we talk about the ASR technique - it also involves with several parameters, that is, Speech rate, Articulation rate, Silent pause duration, Filled pause duration, etc.

- As per the CogMo items, justifications for giving one or two points for each item should be clearly indicated. Does the given rule depend on evidence-based research?

- Please provide details of the circumstances during testing the participants.

- Flowchart of the ASR processing should be incorporated to aid understanding.

- The justification for using MMSE instead of MOCA should be indicated because previous studies suggested that MOCA outperformed MMSE for detecting age-related physiological decline of cognitive functioning (e.g., Aiello et al., 2022; Manser & de Bruin, 2024)

Results

- The authors should provide results of basic statistical assumptions (normality, homogeneity of variances, skewness, kurtosis, etc.) before conducting main analyses.

- There is statistically significant for mean ages between two groups. How do the authors control this effect?

- Is there any missing value showing in the dataset, especially for the ASR scoring?

- A very strong correlation between manual and the ASR scoring in the current study. It would be better off if the authors could clarify how to calculate the total score for the ASR scoring. At the moment, it is only mentioned in the main text that the authors employed 'the Google Cloud’s speech-to-text application programming interface'.

Discussion

- How do we utilize the CogMo to monitor multi-domain cognitive functions? Since it relies on the overall score.

- How did you believe that CogMo showed a high diagnostic accuracy since the current study used MMSE as a gold standard instead of medical diagnostic from physicians or neurologists?

Reviewer #2: The author propose a self-administered cognitive test name CogMo using ASR and effectively demonstrated its usability to predict MMSE score. In total the CogMO has 8 components, and subjects are graded for each component, teh results indicate the score of each component is highly correlated with MMSE.

Below are some comments to consider

1) Data availability statement need to clearly mentioned.

2) It was a bit confusing that that is an automated system or needs human labelling, make it clear

3) How the scoring of different component will be done automatically in the future, through analysis of facial and spoken content or eye tracking devices.

4) making it fully automatic will lead to reduction in results, please discuss

5) MMSE prediction is also performed through speech of picture description tasks, please discuss and add references

6) Introduction is lacking discussion about the prediction of MMSE in other languages.

7) The limitation of study is Korean language, please discuss

8) what speech recognition models are used (Whisper is teh state of the art but requires resources), and how it affects the deployment of those models in low-resource dives like tablets or mobile phones. Please discuss

9) Also in introduction discussion, what advantages CogMO is brining for elders, is there any evidence to prove it is better than MMSE

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Reviewer #1: Yes: Asst. Prof. Dr. Peera Wongupparaj

Reviewer #2: No

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Development and validation of a self-administered computerized cognitive assessment based on automatic speech recognition

PONE-D-24-21463R1

Dear Dr. Oh,

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,

Tamlyn Julie Watermeyer

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Dear Dr Oh and team,

Thank you for addressing the Reviewer comments in turn. The manuscript is now approved.

Best wishes,

Tam

Reviewers' comments: