Disfluency Detection with a Semi-Markov Model and Prosodic Features

Abstract

We present a discriminative model for detecting disfluencies in spoken language transcripts. Structurally, our model is a semi-Markov conditional random field with features targeting characteristics unique to speech repairs. This gives a significant performance improvement over standard chain-structured CRFs that have been employed in past work. We then incorporate prosodic features over silences and relative word duration into our semi-CRF model, resulting in further performance gains; moreover, these features are not easily replaced by discrete prosodic indicators such as ToBI breaks. Our final system, the semi-CRF with prosodic information, achieves an F-score of 85.4, which is 1.3 F 1 better than the best prior reported F-score on this dataset.