LSTM for punctuation restoration in speech transcripts

The output of automatic speech recognition systems is generally an unpunctuated stream of words which is hard to process for both humans and machines. We present a two-stage recurrent neural network based model using long short-term memory units to restore punctuation in speech transcripts. In the first stage, textual features are learned on a large text corpus. The second stage combines textual features with pause durations and adapts the model to speech domain. Our approach reduces the number of punctuation errors by up to 16.9% when compared to a decision tree that combines hidden-event language model posteriors with inter-word pause information, having largest improvements in period restoration.