Rescoring Teacher Outputs with Decoded Utterances for Knowledge Distillation in Automatic Speech Recognition

Abstract

Automatic Speech Recognition requires large amounts of training data to achieve good results. As this hand-labeling is both slow and expensive, the utilization of untranscribed speech has been explored for the task. This generally involves training a teacher-model on the available transcribed speech, and training a student model either directly on the latent representations of the teacher, or on the decoded output. We propose to combine these two approaches, and rescore the predictions of the teacher based on the decoded output. The probability of a decoded sentence, and how it corresponds with the probability distribution output of the teacher, affects the rescoring. When training our student model and evaluating using our proposed method, we find it gives up to 8.6% relative improvement in character error rate, and 5.4% relative improvement in word error rate over our strongest baseline.