3M: An Effective Multi-view, Multi-granularity, and Multi-aspect Modeling Approach to English Pronunciation Assessment

Author:

Chao Fu-An1,Lo Tien-Hong1,Wu Tzu-I2,Sung Yao-Ting3,Chen Berlin2

Affiliation:

1. Research Center for Psychological and Educational Testing, National Taiwan Normal University,Taiwan

2. National Taiwan Normal University,Department of Computer Science and Information Engineering,Taiwan

3. National Taiwan Normal University,Department of Educational Psychology and Counseling,Taiwan

Publisher

IEEE

Reference35 articles.

1. WavLM: Large-scale self-supervised pretraining for full stack speech processing;chen;ArXiv Preprint,2021

2. HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units

3. Improving perceptual quality by phone-fortified perceptual loss for speech enhancement;hsieh;ArXiv Preprint,2020

4. wav2vec 2.0: A framework for self-supervised learning of speech representations;baevski;ArXiv Preprint,2020

5. Deep Feature Transfer Learning for Automatic Pronunciation Assessment

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3. Variational Gaussian Process Data Uncertainty;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16

4. Preserving Phonemic Distinctions For Ordinal Regression: A Novel Loss Function For Automatic Pronunciation Assessment;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16

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