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In multi-lingual societies, where multiple languages are spoken in a smallgeographic vicinity, informal conversations often involve mix of languages.Existing speech technologies may be inefficient in extracting information fromsuch conversations, where the speech data is rich in diversity with multiplelanguages and speakers. The DISPLACE (DIarization of SPeaker and LAnguage inConversational Environments) challenge constitutes an open-call for evaluatingand bench-marking the speaker and language diarization technologies on thischallenging condition. The challenge entailed two tracks: Track-1 focused onspeaker diarization (SD) in multilingual situations while, Track-2 addressedthe language diarization (LD) in a multi-speaker scenario. Both the tracks wereevaluated using the same underlying audio data. To facilitate this evaluation,a real-world dataset featuring multilingual, multi-speaker conversationalfar-field speech was recorded and distributed. Furthermore, a baseline systemwas made available for both SD and LD task which mimicked the state-of-art inthese tasks. The challenge garnered a total of $42$ world-wide registrationsand received a total of $19$ combined submissions for Track-1 and Track-2. Thispaper describes the challenge, details of the datasets, tasks, and the baselinesystem. Additionally, the paper provides a concise overview of the submittedsystems 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 beforewide-spread commercial deployment on such conversations.

Summary of the DISPLACE Challenge 2023 - DIarization of SPeaker and LAnguage in Conversational Environments