@Article{Supelec4,
author = {Olivier Pietquin and Thierry Dutoit},
title = {{A Probabilistic Framework for Dialog Simulation and Optimal Strategy Learning}},
journal = {IEEE Transactions on Audio, Speech and Language Processing},
year = {2006},
volume = {14},
number = {2},
pages = {589-599},
month = {mar},
url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp\'etp=\&arnumber=1597262\&isnumber=33593},
abstract = {The design of Spoken Dialog Systems cannot be considered as the
simple combination of speech processing technologies. Indeed,
speech-based interface design has been an expert job for a long
time. It necessitates good skills in speech technologies and
low-level programming. Moreover, rapid development and
reusability of previously designed systems remains uneasy. This
makes optimality and objective evaluation of design very
difficult. The design process is therefore a cyclic process
composed of prototype releases, user satisfaction surveys, bug
reports and refinements. It is well known that human
intervention for testing is time-consuming and above all very
expensive. This is one of the reasons for the recent interest
in dialog simulation for evaluation as well as for design
automation and optimization. In this paper we expose a
probabilistic framework for a realis-tic simulation of spoken
dialogs in which the major components of a dialog system are
modeled and parameterized thanks to inde-pendent data or expert
knowledge. Especially, an Automatic Speech Recognition (ASR)
system model and a User Model (UM) have been developed. The ASR
model, based on articulatory simi-larities in language models,
provides task-adaptive performance prediction and Confidence
Level (CL) distribution estimation. The user model relies on
the Bayesian Networks (BN) paradigm and is used both for user
behavior modeling and Natural Lan-guage Understanding (NLU)
modeling. The complete simulation framework has been used to
train a reinforcement-learning agent on two different tasks.
These experiments helped to point out several potentially
problematic dialog scenarios.
},
hal = {hal-00207952}
}