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：尽管对话系统技术竞赛（DSTC）推动了对话系统的显著进步，但构建具备语音接口的鲁棒性任务型对话系统仍是关键挑战之一。目前多数进展集中于文本对话系统，这得益于丰富的书面语数据集，而口语对话数据集则极为稀缺。然而，从Siri和Alexa等语音助手的应用可见，将文本对话系统的成功迁移至口语对话具有重要实践价值。本文描述了我们在构建高性能模型过程中的工程实践，该模型参与了DSTC11语音感知对话系统技术挑战赛道。模型包含三个核心模块：（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），通过槽位描述估计槽位及其取值；（3）后处理模块，用于修正估计槽位值的差错。实验表明，显式引入自动语音识别纠错模块、后处理及数据增强技术，对于将文本对话状态追踪器适配至口语对话语料库至关重要。