Iterative Pseudo-Labeling for Speech Recognition
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
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Datasets
LibriSpeech
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Speech Recognition | LibriSpeech test-clean | Conv + Transformer AM + Iterative Pseudo-Labeling (n-gram LM + Transformer Rescoring) | Word Error Rate (WER) | 2.10 | # 21 | |
| Speech Recognition | LibriSpeech test-other | Conv + Transformer AM + Iterative Pseudo-Labeling (n-gram LM + Transformer Rescoring) | Word Error Rate (WER) | 3.83 | # 11 |
