Research Issues for the Next Generation Spoken Dialogue Systems

1999 ◽  
pp. 1-9 ◽  
Author(s):  
E. Nöth ◽  
F. Gallwitz ◽  
M. Aretoulaki ◽  
J. Haas ◽  
S. Harbeck ◽  
...  
Keyword(s):  
Dialogue Systems ◽  
Next Generation ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue ◽  
Research Issues

Research Issues for the Next Generation Spoken Dialogue Systems Revisited

2001 ◽  
pp. 341-348 ◽  
Author(s):  
E. Nöth ◽  
M. Boros ◽  
J. Fischer ◽  
F. Gallwitz ◽  
J. Haas ◽  
...  
Keyword(s):  
Dialogue Systems ◽  
Next Generation ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue ◽  
Research Issues

SemEval-2014 Task 2: Grammar Induction for Spoken Dialogue Systems

2014 ◽  
Author(s):  
Ioannis Klasinas ◽  
Elias Iosif ◽  
Katerina Louka ◽  
Alexandros Potamianos
Keyword(s):  
Dialogue Systems ◽  
Grammar Induction ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue

Spoken dialogue systems

2014 ◽  
Vol 21 (1) ◽  
pp. 46-51 ◽  
Author(s):  
Pierre Lison ◽  
Raveesh Meena
Keyword(s):  
Dialogue Systems ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue

scholarly journals Characterizing and Predicting Corrections in Spoken Dialogue Systems

2006 ◽  
Vol 32 (3) ◽  
pp. 417-438 ◽  
Author(s):  
Diane Litman ◽  
Julia Hirschberg ◽  
Marc Swerts
Keyword(s):  
Speech Recognition ◽  
Predictive Power ◽  
Classification Error ◽  
Dialogue Systems ◽  
Dialogue System ◽  
Spoken Dialogue Systems ◽  
Experimental Conditions ◽  
Spoken Dialogue ◽  
Spoken Dialogue System ◽  
Recognition Errors

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%.

Real user evaluation of spoken dialogue systems using Amazon Mechanical Turk

2011 ◽  
Author(s):  
F. Jurčíček ◽  
S. Keizer ◽  
Milica Gašić ◽  
François Mairesse ◽  
B. Thomson ◽  
...  
Keyword(s):  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
User Evaluation ◽  
Dialogue Systems ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue
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