Semi-Supervised Speech Recognition via Local Prior Matching

• URL: http://arxiv.org/abs/2002.10336v1
• Date: Mon, 24 Feb 2020 16:07:11 GMT
• Title: Semi-Supervised Speech Recognition via Local Prior Matching
• Authors: Wei-Ning Hsu, Ann Lee, Gabriel Synnaeve, Awni Hannun
• Abstract summary: Local prior matching is a semi-supervised objective that distills knowledge from a strong prior. We demonstrate that LPM is theoretically well-native, simple to implement, and superior to existing knowledge distillation techniques.
• Score: 42.311823406287864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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