@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
}
}