Do Self-Supervised Speech and Language Models Extract Similar Representations as Human Brain?-Reference-Cited by-同舟云学术

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Do Self-Supervised Speech and Language Models Extract Similar Representations as Human Brain?

Published:2024-04-14 Issue: Volume:1 Page:2225-2229
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Chen Peili¹,He Linyang²,Fu Li³,Fan Lu³,Chang Edward F.⁴,Li Yuanning¹

Affiliation:

1. ShanghaiTech University,School of Biomedical Engineering,Shanghai,China

2. University of Michigan,Department of Electrical Engineering and Computer Science,Ann Arbor,MI,USA

3. JD AI Research,Beijing,China

4. University of California,Department of Neurological Surgery,San Francisco,CA,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10446334.pdf?arnumber=10446334

Reference23 articles.

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

2. Deep Contextualized Word Representations

3. Layer-Wise Analysis of a Self-Supervised Speech Representation Model

4. Language models are unsupervised multitask learners;Radford;OpenAI blog,2019

5. wav2vec 2.0: A framework for self-supervised learning of speech representations;Baevski;Advances in neural information processing systems,2020

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