PONE-D-24-60568A Study on Phonemes Recognition Method for Mandarin Pronunciation Based on Improved Zipformer-RNN-T(Pruned) ModelingPLOS ONE

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Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: The proposed model demonstrates significant advancements in recognition accuracy and real-time processing capabilities, achieving WERs of 1.92% on the Dev set and 2.12% on the Test set with a real-time factor (RTF) of 0.002. These results highlight the model's practical utility for real-time applications, such as voice assistants and live translation systems. The use of 61.1M parameters ensures a lightweight architecture that balances accuracy with computational efficiency, making it highly suitable for deployment in resource-constrained environments. Furthermore, the model’s robustness in noisy environments is emphasized through experiments using the MUSAN dataset, where it maintains low WERs even under challenging acoustic conditions. This adaptability to diverse environments enhances its applicability in real-world scenarios with prevalent noise interference.

However, the paper exhibits several limitations. The introduction lacks clarity and a logical flow, particularly in justifying the necessity of the proposed model. Although challenges in phoneme recognition are discussed, the connection between these challenges and the choice of the Zipformer variant is insufficiently articulated. Additionally, the importance of RTF optimization for real-time Chinese phoneme recognition is not adequately supported with concrete examples or detailed reasoning. Another major drawback is the reliance on the AISHELL1-PHONEME dataset, which limits the generalizability of the model. Evaluating performance solely on a single dataset raises concerns about its effectiveness in different linguistic contexts, such as conversational or regional Mandarin. Furthermore, the paper does not sufficiently emphasize the model's relevance to Chinese phoneme recognition, as specific challenges like tonal variations and real-time processing requirements are not clearly tied to the proposed approach.

To improve the study, incorporating additional datasets such as L2-ARCTIC or augmenting AISHELL1-PHONEME with multilingual or augmented data would strengthen the model’s validation. If other datasets are unavailable, data augmentation techniques could be explored to simulate diverse linguistic contexts. Additionally, the introduction should more effectively highlight the specific challenges in Chinese phoneme recognition and how the Zipformer variant addresses these issues, supported by clear examples and reasoning. Despite these limitations, the model presents promising results in accuracy, efficiency, and noise robustness, making it a strong candidate for real-world applications. Enhancing the scope of evaluation and refining the introduction would further solidify the paper’s contributions to Mandarin phoneme recognition research.

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Reviewer #1: No

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A Study on Phonemes Recognition Method for Mandarin Pronunciation Based on Improved Zipformer-RNN-T(Pruned) Modeling

PONE-D-24-60568R1

Dear Dr. yu,

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 Zhang

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The authors have adequately addressed the reviewer's comments.

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