scholarly journals Florence: a dialogue manager framework for spoken dialogue systems

Author(s):  
Giuseppe Di Fabbrizio ◽  
Charles Lewis
2002 ◽  
Vol 16 ◽  
pp. 293-319 ◽  
Author(s):  
M. A. Walker ◽  
I. Langkilde-Geary ◽  
H. Wright Hastie ◽  
J. Wright ◽  
A. Gorin

Spoken dialogue systems promise efficient and natural access to a large variety of information sources and services from any phone. However, current spoken dialogue systems are deficient in their strategies for preventing, identifying and repairing problems that arise in the conversation. This paper reports results on automatically training a Problematic Dialogue Predictor to predict problematic human-computer dialogues using a corpus of 4692 dialogues collected with the 'How May I Help You' (SM) spoken dialogue system. The Problematic Dialogue Predictor can be immediately applied to the system's decision of whether to transfer the call to a human customer care agent, or be used as a cue to the system's dialogue manager to modify its behavior to repair problems, and even perhaps, to prevent them. We show that a Problematic Dialogue Predictor using automatically-obtainable features from the first two exchanges in the dialogue can predict problematic dialogues 13.2% more accurately than the baseline.


2014 ◽  
Author(s):  
Ioannis Klasinas ◽  
Elias Iosif ◽  
Katerina Louka ◽  
Alexandros Potamianos

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

2006 ◽  
Vol 32 (3) ◽  
pp. 417-438 ◽  
Author(s):  
Diane Litman ◽  
Julia Hirschberg ◽  
Marc Swerts

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%.


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