TY - JOUR
T1 - Naturalistic dialogue management for noisy speech recognition
AU - Passonneau, Rebecca J.
AU - Epstein, Susan L.
AU - Ligorio, Tiziana
N1 - Funding Information:
Manuscript received July 23, 2012; revised October 16, 2012; accepted November 10, 2012. Date of publication November 27, 2012; date of current version January 03, 2013. The Loqui project is funded by National Science Foundation awards IIS-0745369, IIS-0744904 and IIS-084966. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Steve Young.
PY - 2012
Y1 - 2012
N2 - With naturalistic dialogue management, a spoken dialogue system behaves as a human would under similar conditions. This paper reports on an experiment to develop naturalistic clarification strategies for noisy speech recognition in the context of spoken dialogue systems. We collected a wizard-of-Oz corpus in which human wizards with access to a rich set of clarification actions made clarification decisions online, based on human-readable versions of system data. The experiment compares an evaluation of calls to a baseline system in a library domain with calls to an enhanced version of the system. The new system has a clarification module based on the wizard data that is a decision tree constructed from three machine-learned models. It replicates the wizards' ability to ground partial understandings of noisy input and to build upon them. The enhanced system has a significantly higher rate of task completion, greater task success and improved efficiency.
AB - With naturalistic dialogue management, a spoken dialogue system behaves as a human would under similar conditions. This paper reports on an experiment to develop naturalistic clarification strategies for noisy speech recognition in the context of spoken dialogue systems. We collected a wizard-of-Oz corpus in which human wizards with access to a rich set of clarification actions made clarification decisions online, based on human-readable versions of system data. The experiment compares an evaluation of calls to a baseline system in a library domain with calls to an enhanced version of the system. The new system has a clarification module based on the wizard data that is a decision tree constructed from three machine-learned models. It replicates the wizards' ability to ground partial understandings of noisy input and to build upon them. The enhanced system has a significantly higher rate of task completion, greater task success and improved efficiency.
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U2 - 10.1109/JSTSP.2012.2229964
DO - 10.1109/JSTSP.2012.2229964
M3 - Article
AN - SCOPUS:84872129781
SN - 1932-4553
VL - 6
SP - 928
EP - 942
JO - IEEE Journal on Selected Topics in Signal Processing
JF - IEEE Journal on Selected Topics in Signal Processing
IS - 8
M1 - 6362157
ER -