What Do Self-Supervised Speech and Speaker Models Learn? New Findings from a Cross Model Layer-Wise Analysis-Reference-Cited by-同舟云学术

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What Do Self-Supervised Speech and Speaker Models Learn? New Findings from a Cross Model Layer-Wise Analysis

Published:2024-04-14 Issue: Volume: Page:10166-10170
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Ashihara Takanori¹,Delcroix Marc¹,Moriya Takafumi¹,Matsuura Kohei¹,Asami Taichi¹,Ijima Yusuke¹

Affiliation:

1. NTT Corporation,Japan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10446422.pdf?arnumber=10446422

Reference34 articles.

1. Self-Supervised Speech Representation Learning: A Review

2. wav2vec 2.0: A framework for self-supervised learning of speech representations;Baevski

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

4. WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing

5. SUPERB: Speech Processing Universal PERformance Benchmark

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Unveiling the Linguistic Capabilities of a Self-Supervised Speech Model Through Cross-Lingual Benchmark and Layer- Wise Similarity Analysis;IEEE Access;2024

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