Automatic Comma Insertion of Lecture Transcripts Based on Multiple Annotations

To enhance readability and usability of speech recognition results, automatic punctuation is an essential process. In this paper, we address automatic comma prediction based on conditional random fields (CRF) using lexical, syntactic and pause information. Since there is large disagreement in comma insertion between humans, we model individual tendencies of punctuation using annotations given by multiple annotators, and combine these models by voting and interpolation frameworks. Experimental evaluations on real lecture speech demonstrated that the combination of individual punctuation models achieves higher prediction accuracy for commas agreed by all annotators and those given by individual annotators.