  Though Dialogue State Tracking (DST) is a core component of spoken dialogue
systems, recent work on this task mostly deals with chat corpora, disregarding
the discrepancies between spoken and written language.In this paper, we propose
OLISIA, a cascade system which integrates an Automatic Speech Recognition (ASR)
model and a DST model. We introduce several adaptations in the ASR and DST
modules to improve integration and robustness to spoken conversations.With
these adaptations, our system ranked first in DSTC11 Track 3, a benchmark to
evaluate spoken DST. We conduct an in-depth analysis of the results and find
that normalizing the ASR outputs and adapting the DST inputs through data
augmentation, along with increasing the pre-trained models size all play an
important role in reducing the performance discrepancy between written and
spoken conversations.
