1. DISPLACE Challenge: DIarization of SPeaker and LAnguage in Conversational Environments
- Author
-
Baghel, Shikha, Ramoji, Shreyas, Sidharth, H, Ranjana, Singh, Prachi, Jain, Somil, Chowdhuri, Pratik Roy, Kulkarni, Kaustubh, Padhi, Swapnil, Vijayasenan, Deepu, and Ganapathy, Sriram
- Subjects
Signal Processing (eess.SP) ,FOS: Computer and information sciences ,Sound (cs.SD) ,Audio and Speech Processing (eess.AS) ,FOS: Electrical engineering, electronic engineering, information engineering ,Electrical Engineering and Systems Science - Signal Processing ,Computer Science - Sound ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
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.
- Published
- 2023
- Full Text
- View/download PDF