  In the recent trend of semi-supervised speech recognition, both
self-supervised representation learning and pseudo-labeling have shown
promising results. In this paper, we propose a novel approach to combine their
ideas for end-to-end speech recognition model. Without any extra loss function,
we utilize the Gradient Mask to optimize the model when training on
pseudo-label. This method forces the speech recognition model to predict from
the masked input to learn strong acoustic representation and make training
robust to label noise. In our semi-supervised experiments, the method can
improve the model performance when training on pseudo-label and our method
achieved competitive results comparing with other semi-supervised approaches on
the Librispeech 100 hours experiments.
