In multi-lingual societies, where multiple languages are spoken in a small
geographic vicinity, informal conversations often involve mix of languages.
Existing speech technologies may be inefficient in extracting information from
such conversations, where the speech data is rich in diversity with multiple
languages and speakers. The DISPLACE (DIarization of SPeaker and LAnguage in
Conversational Environments) challenge constitutes an open-call for evaluating
and bench-marking the speaker and language diarization technologies on this
challenging condition. The challenge entailed two tracks: Track-1 focused on
speaker diarization (SD) in multilingual situations while, Track-2 addressed
the language diarization (LD) in a multi-speaker scenario. Both the tracks were
evaluated using the same underlying audio data. To facilitate this evaluation,
a real-world dataset featuring multilingual, multi-speaker conversational
far-field speech was recorded and distributed. Furthermore, a baseline system
was made available for both SD and LD task which mimicked the state-of-art in
these tasks. The challenge garnered a total of $42$ world-wide registrations
and received a total of $19$ combined submissions for Track-1 and Track-2. This
paper describes the challenge, details of the datasets, tasks, and the baseline
system. Additionally, the paper provides a concise overview of the submitted
systems in both tracks, with an emphasis given to the top performing systems.
The paper also presents insights and future perspectives for SD and LD tasks,
focusing on the key challenges that the systems need to overcome before
wide-spread commercial deployment on such conversations.

在多语言社会中，非正式对话经常涉及多种语言的混合使用。DISPLACE 挑战对多语言和说话者鉴别技术进行了评估和基准测试，其中 Track-1 专注于多语境的说话者鉴别，而 Track-2 则针对多说话者情境的语言鉴别。该论文详述了挑战、数据集、任务和基准系统，并提供了顶尖系统的概述，强调了在这些对话中系统在普遍商业应用之前需要克服的主要挑战。