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.

翻译：尽管对话状态跟踪（DST）是口语对话系统的核心组件，但近期该领域研究大多针对聊天语料库，忽视了口语与书面语之间的差异。本文提出OLISIA级联系统，集成了自动语音识别（ASR）模型与DST模型。我们在ASR和DST模块中引入多项适应性调整，以提升系统整合性及对口语对话的鲁棒性。通过上述改进，本系统在DSTC11 Track 3（口语DST评估基准）中位列第一。我们对实验结果进行深度分析后发现：规范化ASR输出、通过数据增强适配DST输入，以及扩大预训练模型规模，均对缩小书面与口语对话间的性能差异具有重要作用。