Self-Supervised Learning of Neural Speech Representations From Unlabeled Intracranial Signals
Neuroprosthetics have demonstrated the potential to decode speech from intracranial brain signals, and hold promise for one day returning the ability to speak to those who have lost it. However, data in this domain is scarce, highly variable, and costly to label for supervised modeling. In order to...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9992205/ |