| Speech Self-Supervised Learning |
| =============================== |
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| Self-Supervised Learning (SSL) refers to the problem of learning without explicit labels. As |
| any learning process require feedback, without explit labels, SSL derives supervisory signals from |
| the data itself. The general ideal of SSL is to predict any hidden part (or property) of the input |
| from observed part of the input (e.g., filling in the blanks in a sentence or predicting whether |
| an image is upright or inverted). |
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| SSL for speech/audio understanding broadly falls into either contrastive or reconstruction |
| based approaches. In contrastive methods, models learn by distinguishing between true and distractor |
| tokens (or latents). Examples of contrastive approaches are Contrastive Predictive Coding (CPC), |
| Masked Language Modeling (MLM) etc. In reconstruction methods, models learn by directly estimating |
| the missing (intentionally leftout) portions of the input. Masked Reconstruction, Autoregressive |
| Predictive Coding (APC) are few examples. |
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| In the recent past, SSL has been a major benefactor in improving Acoustic Modeling (AM), i.e., the |
| encoder module of neural ASR models. Here too, majority of SSL effort is focused on improving AM. |
| While it is common that AM is the focus of SSL in ASR, it can also be utilized in improving other parts of |
| ASR models (e.g., predictor module in transducer based ASR models). |
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| In NeMo, we provide two types of SSL models, `Wav2Vec-BERT <https: |
| The training script for them can be found in `https: |
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| The full documentation tree is as follows: |
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| .. toctree:: |
| :maxdepth: 8 |
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| models |
| datasets |
| results |
| configs |
| api |
| resources |
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| .. include:: resources.rst |
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