We introduce a new dataset of conversational speech representing English from
India, Nigeria, and the United States. The Multi-Dialect Dataset of Dialogues
(MD3) strikes a new balance between open-ended conversational speech and
task-oriented dialogue by prompting participants to perform a series of short
information-sharing tasks. This facilitates quantitative cross-dialectal
comparison, while avoiding the imposition of a restrictive task structure that
might inhibit the expression of dialect features. Preliminary analysis of the
dataset reveals significant differences in syntax and in the use of discourse
markers. The dataset, which will be made publicly available with the
publication of this paper, includes more than 20 hours of audio and more than
200,000 orthographically-transcribed tokens.

本文介绍了一个新的跨方言语音数据集，用于英语印度、尼日利亚和美国口音。该数据集通过提示参与者执行一系列短的信息分享任务来平衡开放式对话和面向任务的对话，以促进量化的跨方言比较，并避免对方言特征表现的限制性任务结构的强制性。初步分析表明该数据集在语法和话语标记的使用上有显着差异。该数据集包括超过 20 个小时的音频和超过 200,000 个正字法转录的标记，并已公开发布。

MD3: 多方言对话数据集

MD3: The Multi-Dialect Dataset of Dialogues

Question answering (QA) systems are now available through numerous commercial
applications for a wide variety of domains, serving millions of users that
interact with them via speech interfaces. However, current benchmarks in QA
research do not account for the errors that speech recognition models might
introduce, nor do they consider the language variations (dialects) of the
users. To address this gap, we augment an existing QA dataset to construct a
multi-dialect, spoken QA benchmark on five languages (Arabic, Bengali, English,
Kiswahili, Korean) with more than 68k audio prompts in 24 dialects from 255
speakers. We provide baseline results showcasing the real-world performance of
QA systems and analyze the effect of language variety and other sensitive
speaker attributes on downstream performance. Last, we study the fairness of
the ASR and QA models with respect to the underlying user populations. The
dataset, model outputs, and code for reproducing all our experiments are
available: this https URL

本文介绍了一个针对多个语言方言创建的语音问答数据集，分析了语音识别错误和方言对问答系统性能的影响，并且研究了不同用户群体的公平性问题。