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Characterizing and predicting corrections in spoken dialogue systems

Litman, D and Swerts, M and Hirschberg, J (2006) Characterizing and predicting corrections in spoken dialogue systems. Computational Linguistics, 32 (3). 417 - 438. ISSN 0891-2017

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Abstract

This article focuses on the analysis and prediction of corrections, defined as turns where a user tries to correct a prior error made by a spoken dialogue system. We describe our labeling procedure of various corrections types and statistical analyses of their features in a corpus collected from a train information spoken dialogue system. We then present results of machine-learning experiments designed to identify user corrections of speech recognition errors. We investigate the predictive power of features automatically computable from the prosody of the turn, the speech recognition process, experimental conditions, and the dialogue history. Our best-performing features reduce classification error from baselines of 25.70-28.99% to 15.72%. © 2006 Association for Computational Linguistics.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Litman, Ddlitman@pitt.eduDLITMAN
Swerts, M
Hirschberg, J
Centers: University Centers > Learning Research and Development Center (LRDC)
Date: 20 September 2006
Date Type: Publication
Journal or Publication Title: Computational Linguistics
Volume: 32
Number: 3
Page Range: 417 - 438
DOI or Unique Handle: 10.1162/coli.2006.32.3.417
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Dietrich School of Arts and Sciences > Intelligent Systems
Refereed: Yes
ISSN: 0891-2017
Date Deposited: 16 Oct 2014 18:15
Last Modified: 02 Feb 2019 15:59
URI: http://d-scholarship.pitt.edu/id/eprint/23243

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