Year: 2013
Type: conference-paper
Abstract: This paper describes a statistical corpus study of self-repairs in the disfluency- annotated Switchboard corpus which examines the time-linear nature of self-repair processing for annotators and listeners in dialogue. The study suggests a strictly lo- cal detection and processing mechanism for self-repairs is sufficient, an advantage currently not used effectively under the bonnet of state-of-the-art automatic dis- fluency processing. We then show how simple local fluency measures using mod- ified language models can be strongly indicative of repair onset detection, and how simple information theoretic mea- sures could characterize different classes of repair
Authors: Julian Hough, Matthew Purver
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2531252286