  In the English speech-to-text (STT) machine learning task, acoustic models
are conventionally trained on uncased Latin characters, and any necessary
orthography (such as capitalization, punctuation, and denormalization of
non-standard words) is imputed by separate post-processing models. This adds
complexity and limits performance, as many formatting tasks benefit from
semantic information present in the acoustic signal but absent in
transcription. Here we propose a new STT task: end-to-end neural transcription
with fully formatted text for target labels. We present baseline
Conformer-based models trained on a corpus of 5,000 hours of professionally
transcribed earnings calls, achieving a CER of 1.7. As a contribution to the
STT research community, we release the corpus free for non-commercial use at
https://datasets.kensho.com/datasets/scribe.
