  In this paper we explain how contextual expectations are generated and used
in the task-oriented spoken language understanding system Dialogos. The hard
task of recognizing spontaneous speech on the telephone may greatly benefit
from the use of specific language models during the recognition of callers'
utterances. By 'specific language models' we mean a set of language models that
are trained on contextually appropriated data, and that are used during
different states of the dialogue on the basis of the information sent to the
acoustic level by the dialogue management module. In this paper we describe how
the specific language models are obtained on the basis of contextual
information. The experimental result we report show that recognition and
understanding performance are improved thanks to the use of specific language
models.
