Baghel, S.Ramoji, S.SidharthRanjana, H.Singh, P.Jain, S.Chowdhuri, P.R.Kulkarni, K.Padhi, S.Vijayasenan, D.Ganapathy, S.2026-02-062023Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2023, Vol.2023-August, , p. 3562-35662308457Xhttps://doi.org/10.21437/Interspeech.2023-2367https://idr.nitk.ac.in/handle/123456789/29462In multilingual societies, social conversations often involve code-mixed speech. The current speech technology may not be well equipped to extract information from multi-lingual multispeaker conversations. The DISPLACE challenge entails a first-of-kind task to benchmark speaker and language diarization on the same data, as the data contains multi-speaker conversations in multilingual code-mixed speech. The challenge attempts to highlight outstanding issues in speaker diarization (SD) in multilingual settings with code-mixing. Further, language diarization (LD) in multi-speaker settings also introduces new challenges, where the system has to disambiguate speaker switches with code switches. For this challenge, a natural multilingual, multi-speaker conversational dataset is distributed for development and evaluation purposes. The systems are evaluated on single-channel far-field recordings. We also release a baseline system and report the highlights of the system submissions. © 2023 International Speech Communication Association. All rights reserved.code-mixingconversational speechDISPLACE challengelanguage diarizationSpeaker diarization