In multilingual societies, social conversations often involve code-mixed speech. The current speech technology may not be well equipped to extract information from multi-lingual multi-speaker 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.
@article{arxiv.2303.00830,
title = {DISPLACE Challenge: DIarization of SPeaker and LAnguage in Conversational Environments},
author = {Shikha Baghel and Shreyas Ramoji and Sidharth and Ranjana H and Prachi Singh and Somil Jain and Pratik Roy Chowdhuri and Kaustubh Kulkarni and Swapnil Padhi and Deepu Vijayasenan and Sriram Ganapathy},
journal= {arXiv preprint arXiv:2303.00830},
year = {2023}
}