Papers › Semi-Supervised Speech Recognition via Local Prior Matching
Semi-Supervised Speech Recognition via Local Prior Matching
Wei-Ning Hsu, Ann Lee, Gabriel Synnaeve, Awni Hannun
For sequence transduction tasks like speech recognition, a strong structured prior model encodes rich information about the target space, implicitly ruling out invalid sequences by assigning them low probability. In this work, we propose local prior matching (LPM), a semi-supervised objective that distills knowledge from a strong prior (e.g. a language model) to provide learning signal to a discriminative model trained on unlabeled speech. We demonstrate that LPM is theoretically well-motivated, simple to implement, and superior to existing knowledge distillation techniques under comparable settings. Starting from a baseline trained on 100 hours of labeled speech, with an additional 360 hours of unlabeled data, LPM recovers 54% and 73% of the word error rate on clean and noisy test sets relative to a fully supervised model on the same data.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Speech Recognition | LibriSpeech test-clean | Local Prior Matching (Large Model) | Word Error Rate (WER) | 7.19 | #62 of 64 | Archive leaderboard | report |
| Speech Recognition | LibriSpeech test-other | Local Prior Matching (Large Model, ConvLM LM) | Word Error Rate (WER) | 15.28 | #51 of 53 | Archive leaderboard | report |
| Speech Recognition | LibriSpeech test-other | Local Prior Matching (Large Model) | Word Error Rate (WER) | 20.84 | #53 of 53 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: LPM
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