  We introduce O-1, a new self-training objective to reduce training bias and
unify training and evaluation metrics for speech recognition. O-1 is a faster
variant of Expected Minimum Bayes Risk (EMBR), that boosts the oracle
hypothesis and can accommodate both supervised and unsupervised data. We
demonstrate the effectiveness of our approach in terms of recognition on
publicly available SpeechStew datasets and a large-scale, in-house data set. On
Speechstew, the O-1 objective closes the gap between the actual and oracle
performance by 80\% relative compared to EMBR which bridges the gap by 43\%
relative. O-1 achieves 13\% to 25\% relative improvement over EMBR on the
various datasets that SpeechStew comprises of, and a 12\% relative gap
reduction with respect to the oracle WER over EMBR training on the in-house
dataset. Overall, O-1 results in a 9\% relative improvement in WER over EMBR,
thereby speaking to the scalability of the proposed objective for large-scale
datasets.
