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，但近期相关研究主要聚焦于聊天语料库，忽视了口语与书面语之间的差异。本文提出OLISIA级联系统，该集成了自动语音识别（ASR）模型与DST模型。我们分别在ASR和DST模块中引入多项改进措施，以增强对口语对话的鲁棒性及模块间集成效果。基于这些改进，本系统在评估口语DST性能的DSTC11 Track 3基准测试中位列第一。通过深入分析实验结果发现，对ASR输出进行归一化处理、通过数据增强调整DST输入，以及增大预训练模型规模，均能有效缩小书面语与口语对话之间的性能差距。