@InProceedings{Supelec22,
author = {Olivier Pietquin and Steve Renals},
title = {{ASR System Modeling For Automatic Evaluation And Optimization of Dialogue Systems}},
year = {2002},
booktitle = {{Proceedings of the 27th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2002)}},
volume = {I},
pages = {45-48},
month = {May},
address = {Orlando, (USA, FL)},
url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp\'earnumber=1005671},
isbn = {0-7803-7402-9},
abstract = {Though the field of spoken dialogue systems has developed quickly in the last decade, rapid design of dialogue strategies remains uneasy. Several approaches to the problem of automatic strategy learning have been proposed and the use of reinforcement learning introduced by Levin and Pieraccini (see Pieraccini, R. et al., IEEE Trans. on Speech and Audio Proc., vol.8, p.11-23, 2000) is becoming part of the state of the art in this area. However, the quality of the strategy learned by the system depends on the definition of the optimization criterion and on the accuracy of the environment model. We propose to bring a model of an ASR system into a simulated environment in order to enhance the learned strategy. To do so, we introduced recognition error rates and confidence levels produced by ASR systems in the optimization criterion }
}