  This paper presents Conformer-1, an end-to-end Automatic Speech Recognition
(ASR) model trained on an extensive dataset of 570k hours of speech audio data,
91% of which was acquired from publicly available sources. To achieve this, we
perform Noisy Student Training after generating pseudo-labels for the unlabeled
public data using a strong Conformer RNN-T baseline model. The addition of
these pseudo-labeled data results in remarkable improvements in relative Word
Error Rate (WER) by 11.5% and 24.3% for our asynchronous and realtime models,
respectively. Additionally, the model is more robust to background noise owing
to the addition of these data. The results obtained in this study demonstrate
that the incorporation of pseudo-labeled publicly available data is a highly
effective strategy for improving ASR accuracy and noise robustness.
