  In recent years, studies on automatic speech recognition (ASR) have shown
outstanding results that reach human parity on short speech segments. However,
there are still difficulties in standardizing the output of ASR such as
capitalization and punctuation restoration for long-speech transcription. The
problems obstruct readers to understand the ASR output semantically and also
cause difficulties for natural language processing models such as NER, POS and
semantic parsing. In this paper, we propose a method to restore the punctuation
and capitalization for long-speech ASR transcription. The method is based on
Transformer models and chunk merging that allows us to (1), build a single
model that performs punctuation and capitalization in one go, and (2), perform
decoding in parallel while improving the prediction accuracy. Experiments on
British National Corpus showed that the proposed approach outperforms existing
methods in both accuracy and decoding speed.
