Perturbation-invariant Speech Representation Learning by Online Clustering
Despite success across various tasks, self-supervised speech models face significant challenges in enhancing content-related performance with unlabeled data, requiring substantial computational resources. Meanwhile, learning from clustered discrete units has been shown to facilitate accurate phoneti...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/153784 https://orcid.org/0000-0002-1690-2610 |