Although there have been remarkable advances in dialogue systems through the dialogue systems technology competition (DSTC), it remains one of the key challenges to building a robust task-oriented dialogue system with a speech interface. Most of the progress has been made for text-based dialogue systems since there are abundant datasets with written corpora while those with spoken dialogues are very scarce. However, as can be seen from voice assistant systems such as Siri and Alexa, it is of practical importance to transfer the success to spoken dialogues. In this paper, we describe our engineering effort in building a highly successful model that participated in the speech-aware dialogue systems technology challenge track in DSTC11. Our model consists of three major modules: (1) automatic speech recognition error correction to bridge the gap between the spoken and the text utterances, (2) text-based dialogue system (D3ST) for estimating the slots and values using slot descriptions, and (3) post-processing for recovering the error of the estimated slot value. Our experiments show that it is important to use an explicit automatic speech recognition error correction module, post-processing, and data augmentation to adapt a text-based dialogue state tracker for spoken dialogue corpora.

翻译：尽管通过对话系统技术竞赛在对话系统领域取得了显著进展，构建具有语音接口的鲁棒性任务型对话系统仍是核心挑战之一。现有进展主要集中于基于文本的对话系统，因为存在大量包含书面语料的数据集，而口语对话数据集则极为稀缺。然而，从Siri、Alexa等语音助手系统可见，将现有成功迁移至口语对话具有重要的实践价值。本文阐述了我们在构建高性能模型中的工程实践，该模型参与了DSTC11语音感知对话系统技术挑战赛。本模型由三大核心模块构成：(1)自动语音识别纠错模块，用于弥合口语与文本表述之间的差异；(2)基于文本的对话系统（D3ST），利用槽位描述进行槽值与值的估算；(3)后处理模块，用于修正估算槽值的误差。实验结果表明，显式构建自动语音识别纠错模块、后处理及数据增强对于将文本对话状态追踪器适配至口语对话语料库具有关键作用。