Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction
Self-supervised learning (SSL) has emerged as a promising paradigm for learning flexible speech representations from <i>unlabeled</i> data. By designing <i>pretext tasks</i> that exploit statistical regularities, SSL models can capture <i>useful</i> representation...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
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| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/15/2/114 |