Self-Supervised Learning
Problem Agnostic Speech Encoder +
Introduced by Ravanelli et al. in Multi-task self-supervised learning for Robust Speech RecognitionPASE+ is a problem-agnostic speech encoder that combines a convolutional encoder followed by multiple neural networks, called workers, tasked to solve self-supervised problems (i.e., ones that do not require manual annotations as ground truth). An online speech distortion module is employed, that contaminates the input signals with a variety of random disturbances. A revised encoder is also proposed that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks. Finally, the authors refine the set of workers used in self-supervision to encourage better cooperation.
Source: Multi-task self-supervised learning for Robust Speech Recognition
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Denoising | 1 | 14.29% |
| Emotion Classification | 1 | 14.29% |
| Multi-Task Learning | 1 | 14.29% |
| Speech Denoising | 1 | 14.29% |
| Robust Speech Recognition | 1 | 14.29% |
| Self-Supervised Learning | 1 | 14.29% |
| Speech Recognition | 1 | 14.29% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
