Unediting: Detecting Disfluencies Without Careful Transcripts

Abstract

Speech transcripts often only capture semantic content, omitting disfluencies that can be useful for analyzing social dynamics of a discussion. This work describes steps in building a model that can recover a large fraction of locations where disfluencies were present, by transforming carefully annotated text to match the standard transcription style, introducing a two-stage model for handling different types of disfluencies, and applying semi-supervised learning. Experiments show improvement in disfluency detection on Supreme Court oral arguments, nearly 23% improvement in F1.