Work

Disfluency Detection with a Semi-Markov Model and Prosodic Features

Year: 2015

Type: conference-paper

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.

Source: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)

DOI: 10.3115/v1/n15-1029

Authors: James Ferguson, Greg Durrett, Dan Klein

Institution: University of California, Berkeley

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Defense Advanced Research Projects Agency

OpenAlex ID: W2293765256