Bidirectional LSTM for Automatic Punctuation Restoration
Askars Salimbajevs · Frontiers in artificial intelligence and applications · 2016
The output of generic automatic speech recognition systems consists of raw word sequences without any punctuation symbols. When sequences are longer, it is difficult for humans to read and understand them. Also, many natural language understanding and processing tools expect that input will contain punctuation. We present a bidirectional recurrent neural network for punctuation restoration in speech utterances. The proposed model showed promising results, F1-scores of 0.732 for commas and 0.708 for periods on raw output from a speech recognizer.