  This paper analyses language modeling in spoken dialogue systems for
accessing a database. The use of several language models obtained by exploiting
dialogue predictions gives better results than the use of a single model for
the whole dialogue interaction. For this reason several models have been
created, each one for a specific system question, such as the request or the
confirmation of a parameter.
  The use of dialogue-dependent language models increases the performance both
at the recognition and at the understanding level, especially on answers to
system requests. Moreover other methods to increase performance, like automatic
clustering of vocabulary words or the use of better acoustic models during
recognition, does not affect the improvements given by dialogue-dependent
language models.
  The system used in our experiments is Dialogos, the Italian spoken dialogue
system used for accessing railway timetable information over the telephone. The
experiments were carried out on a large corpus of dialogues collected using
Dialogos.
