Semi-Supervised End-To-End Speech Recognition Via Local Prior Matching

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 an end-to-end model trained on unlabeled speech. We demonstrate that LPM is 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%/82% and 73%/91% of the word error rate on clean and noisy test sets with/without language model rescoring relative to a fully supervised model on the same data.