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.

本文描述了我们在 DSTC11 中参与的具有极大成功的模型的工程努力，该模型由三个主要模块组成：(1) 自动语音识别误差校正，(2) 基于文本的对话系统用于估计插槽和值，(3) 后处理用于恢复估计的插槽值的错误。我们的实验证明了对于口语对话语料库，使用明确的自动语音识别误差校正模块、后处理和数据增强对于调整基于文本的对话状态跟踪器至关重要。

适应口语对话的基于文本的对话状态追踪器

Adapting text-based dialogue state tracker for spoken dialogues

Interacting with a speech interface to query a Question Answering (QA) system
is becoming increasingly popular. Typically, QA systems rely on passage
retrieval to select candidate contexts and reading comprehension to extract the
final answer. While there has been some attention to improving the reading
comprehension part of QA systems against errors that automatic speech
recognition (ASR) models introduce, the passage retrieval part remains
unexplored. However, such errors can affect the performance of passage
retrieval, leading to inferior end-to-end performance. To address this gap, we
augment two existing large-scale passage ranking and open domain QA datasets
with synthetic ASR noise and study the robustness of lexical and dense
retrievers against questions with ASR noise. Furthermore, we study the
generalizability of data augmentation techniques across different domains; with
each domain being a different language dialect or accent. Finally, we create a
new dataset with questions voiced by human users and use their transcriptions
to show that the retrieval performance can further degrade when dealing with
natural ASR noise instead of synthetic ASR noise.

研究了语音接口与问答系统交互过程中的问题，使用数据增强技术来探究自然语音识别噪音对信息检索的影响。结果表明，自然语音识别噪音会对信息检索产生负面影响，提出使用数据增强来解决这个问题。