Finding Sparse Subnetworks in Self-Supervised Speech Recognition and Speech Synthesis
The modern paradigm in speech processing has demonstrated the importance of scale and compute for end-to-end speech recognition and synthesis. For instance, state-of-the-art self-supervised speech representation learning models typically consists of more than 300M model parameters and being trained...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/144615 |