  Pseudo-labeling has recently shown promise in end-to-end automatic speech
recognition (ASR). We study Iterative Pseudo-Labeling (IPL), a semi-supervised
algorithm which efficiently performs multiple iterations of pseudo-labeling on
unlabeled data as the acoustic model evolves. In particular, IPL fine-tunes an
existing model at each iteration using both labeled data and a subset of
unlabeled data. We study the main components of IPL: decoding with a language
model and data augmentation. We then demonstrate the effectiveness of IPL by
achieving state-of-the-art word-error rate on the Librispeech test sets in both
standard and low-resource setting. We also study the effect of language models
trained on different corpora to show IPL can effectively utilize additional
text. Finally, we release a new large in-domain text corpus which does not
overlap with the Librispeech training transcriptions to foster research in
low-resource, semi-supervised ASR
