Adversarial Training of End-to-end Speech Recognition Using a\n Criticizing Language Model
Journal: arXiv (Cornell University)
Year: 2018

In this paper we proposed a novel Adversarial Training (AT) approach for\nend-to-end speech recognition using a Criticizing Language Model (CLM). In this\nway the CLM and the automatic speech recognition (ASR) model can challenge and\nlearn from each other iteratively to improve the performance. Since the CLM\nonly takes the text as input, huge quantities of unpaired text data can be\nutilized in this approach within end-to-end training. Moreover, AT can be\napplied to any end-to-end ASR model u