  Recent technological advances have made it possible to build real-time,
interactive spoken dialogue systems for a wide variety of applications.
However, when users do not respect the limitations of such systems, performance
typically degrades. Although users differ with respect to their knowledge of
system limitations, and although different dialogue strategies make system
limitations more apparent to users, most current systems do not try to improve
performance by adapting dialogue behavior to individual users. This paper
presents an empirical evaluation of TOOT, an adaptable spoken dialogue system
for retrieving train schedules on the web. We conduct an experiment in which 20
users carry out 4 tasks with both adaptable and non-adaptable versions of TOOT,
resulting in a corpus of 80 dialogues. The values for a wide range of
evaluation measures are then extracted from this corpus. Our results show that
adaptable TOOT generally outperforms non-adaptable TOOT, and that the utility
of adaptation depends on TOOT's initial dialogue strategies.
