Iterative Pseudo-Labeling for Speech Recognition
Qiantong Xu, Tatiana Likhomanenko, Jacob Kahn, Awni Hannun, Gabriel Synnaeve, Ronan Collobert
Abstract
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.