@InProceedings{Supelec17,
author = {Olivier Pietquin and Richard Beaufort},
title = {{Comparing ASR Modeling Methods for Spoken Dialogue Simulation and Optimal Strategy Learning}},
year = {2005},
booktitle = {{Proceedings of the 9th European Conference on Speech Communication and Technologies (Interspeech/Eurospeech)}},
publisher = {ISCA},
pages = {861-864},
month = {September},
address = {Lisbon (Portugal)},
url = {http://www.isca-speech.org/archive/interspeech_2005/i05_0861.html},
abstract = {Speech enabled interfaces are nowadays becoming ubiquitous. The
most advanced ones rely on probabilistic pattern matching
systems and especially on automatic speech recognition systems.
Because of their statistical nature, performances of such
systems never reach one hundred percent of correct recognition
results. Performances are linked to environmental noise and to
intra- and inter-speaker variability of course, but also to the
acoustical similarities inside the vocabulary of allowed speech
entries, which is usually contextual in the case of man-machine
dialogue systems. A good dialogue strategy should therefore
dynamically handle the potentiality of recognition errors. In
this paper, we compare different methods to model ASR systems
in the framework of automatic dialogue strategy optimization
and we especially emphasize on a context-dependent ASR modeling
method.}
}