  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.

Audio and Speech Processing;Machine Learning;Signal Processing