Semi-Supervised Learning Methods
Local Prior Matching
Introduced by Hsu et al. in Semi-Supervised Speech Recognition via Local Prior MatchingLocal Prior Matching is a semi-supervised objective for speech recognition that distills knowledge from a strong prior (e.g. a language model) to provide learning signal to a discriminative model trained on unlabeled speech. The LPM objective minimizes the cross entropy between the local prior and the model distribution, and is minimized when $q_{y\mid{x}} = \bar{p}_{y\mid{x}}$. Intuitively, LPM encourages the ASR model to assign posterior probabilities proportional to the linguistic probabilities of the proposed hypotheses.
Source: Semi-Supervised Speech Recognition via Local Prior Matching
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Classification | 1 | 7.69% |
| Medical Image Classification | 1 | 7.69% |
| Image Compression | 1 | 7.69% |
| Visual Prompt Tuning | 1 | 7.69% |
| Management | 1 | 7.69% |
| Computational Efficiency | 1 | 7.69% |
| Experimental Design | 1 | 7.69% |
| Learning with noisy labels | 1 | 7.69% |
| Meta-Learning | 1 | 7.69% |
Usage Over Time
Components
| Component | Type |
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|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
