Most research on task oriented dialog modeling is based on written text
input. However, users interact with practical dialog systems often using speech
as input. Typically, systems convert speech into text using an Automatic Speech
Recognition (ASR) system, introducing errors. Furthermore, these systems do not
address the differences in written and spoken language. The research on this
topic is stymied by the lack of a public corpus. Motivated by these
considerations, our goal in hosting the speech-aware dialog state tracking
challenge was to create a public corpus or task which can be used to
investigate the performance gap between the written and spoken forms of input,
develop models that could alleviate this gap, and establish whether
Text-to-Speech-based (TTS) systems is a reasonable surrogate to the more-labor
intensive human data collection. We created three spoken versions of the
popular written-domain MultiWoz task -- (a) TTS-Verbatim: written user inputs
were converted into speech waveforms using a TTS system, (b) Human-Verbatim:
humans spoke the user inputs verbatim, and (c) Human-paraphrased: humans
paraphrased the user inputs. Additionally, we provided different forms of ASR
output to encourage wider participation from teams that may not have access to
state-of-the-art ASR systems. These included ASR transcripts, word time stamps,
and latent representations of the audio (audio encoder outputs). In this paper,
we describe the corpus, report results from participating teams, provide
preliminary analyses of their results, and summarize the current
state-of-the-art in this domain.

本文涉及针对与书面文本不同的口语输入而设计的任务型对话建模，以解决自动语音识别系统所引入的误差，并针对多轮对话建立了一个公共语料库，研究各种形式的语音输出的性能差距，并给出了初步分析。