@InProceedings{Supelec270,
author = {Oliver Lemon and Olivier Pietquin},
title = {{Machine Learning for Spoken Dialogue Systems}},
year = {2007},
booktitle = {{Proceedings of the 10th European Conference on Speech Communication and Technologies (Interspeech'07)}},
pages = {2685-2688},
month = {August},
address = {Anvers (Belgium)},
url = {http://hal-supelec.archives-ouvertes.fr/hal-00216035/fr/},
abstract = {During the last decade, research in the field of Spoken Dialogue
Systems (SDS) has experienced increasing growth. However, the
design and optimization of SDS is not only about combining
speech and language processing systems such as Automatic Speech
Recognition (ASR), parsers, Natural Language Generation (NLG),
and Text-to-Speech (TTS) synthesis systems. It also requires
the development of dialogue strategies taking at least into
account the performances of these subsystems (and others), the
nature of the task (e.g. form filling, tutoring, robot control,
or database search/browsing), and the user’s behaviour (e.g.
cooperativeness, expertise). Due to the great variability of
these factors, reuse of previous hand-crafted designs is also
made very difficult. For these reasons, statistical machine
learning (ML) methods applied to automatic SDS optimization
have been a leading research area for the last few years. In
this paper, we provide a short review of the field and of
recent advances. }
}