DISPLACE Challenge: DIarization of SPeaker and LAnguage in Conversational Environments

The DISPLACE challenge entails a first-of-kind task to perform speaker and language diarization on the same data, as the data contains multi-speaker social conversations in multilingual code-mixed speech. The challenge attempts to benchmark and improve Speaker Diarization (SD) in multilingual settings and Language Diarization (LD) in multi-speaker settings. For this challenge, a natural multilingual, multi-speaker conversational dataset is distributed for development and evaluation purposes. Automatic systems are evaluated on single-channel far-field recordings containing natural code-mix, code-switch, overlap, reverberation, short turns, short pauses, and multiple dialects of the same language. A total of 60 teams from industry and academia have registered for this challenge.