@InProceedings{Supelec547,
author = {Olivier Pietquin and Stéphane Rossignol and Michel Ianotto},
title = {{Training Bayesian networks for realistic man-machine spoken dialogue simulation}},
year = {2009},
booktitle = {{Proceedings of the 1rst International Workshop on Spoken Dialogue Systems Technology (IWSDS 2009)}},
month = {December},
note = {4 pages},
address = {Irsee (Germany)},
abstract = {Data collection and annotation are generally required to design
or assess spoken dialogue systems. Yet, this is a very time
consuming
and expensive process. For these reasons, user simulation has
become an important trend of research in the field of spoken
dialogue
systems. The general problem of user simulation is thus to
produce as
many as necessary natural, various and consistent interactions
from as
few data as possible. In this paper, we propose a user
simulation method
based on Bayesian networks (BN) that is able to produce
consistent
interactions in terms of user goal and dialogue history. The
model as
been introduced in previous work but parameters were hand-tuned
and
it was assessed in the framework of automatic learning of
optimal dialogue
strategies. In this paper, the BN is trained on a database of
1234
human-machine dialogues in the TownInfo domain (a tourist
information
application). Experiments with a state-of-the-art dialogue
system
(REALL-DUDE/DIPPER/OAA) have been realized and results in terms
of dialog statistics are presented.}
}