Faculty Publications
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Item The DISPLACE Challenge 2023 - DIarization of SPeaker and LAnguage in Conversational Environments(International Speech Communication Association, 2023) Baghel, S.; Ramoji, S.; Sidharth; Ranjana, H.; Singh, P.; Jain, S.; Chowdhuri, P.R.; Kulkarni, K.; Padhi, S.; Vijayasenan, D.; Ganapathy, S.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 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.Item Summary of the DISPLACE challenge 2023-DIarization of SPeaker and LAnguage in Conversational Environments(Elsevier B.V., 2024) Baghel, S.; Ramoji, S.; Jain, S.; Chowdhuri, P.R.; Singh, P.; Vijayasenan, D.; Ganapathy, S.In multi-lingual societies, where multiple languages are spoken in a small geographic vicinity, informal conversations often involve mix of languages. Existing speech technologies may be inefficient in extracting information from such conversations, where the speech data is rich in diversity with multiple languages and speakers. The DISPLACE (DIarization of SPeaker and LAnguage in Conversational Environments) challenge constitutes an open-call for evaluating and bench-marking the speaker and language diarization technologies on this challenging condition. To facilitate this challenge, a real-world dataset featuring multilingual, multi-speaker conversational far-field speech was recorded and distributed. The challenge entailed two tracks: Track-1 focused on speaker diarization (SD) in multilingual situations while, Track-2 addressed the language diarization (LD) in a multi-speaker scenario. Both the tracks were evaluated using the same underlying audio data. Furthermore, a baseline system was made available for both SD and LD task which mimicked the state-of-art in these tasks. The challenge garnered a total of 42 world-wide registrations and received a total of 19 combined submissions for Track-1 and Track-2. This paper describes the challenge, details of the datasets, tasks, and the baseline system. Additionally, the paper provides a concise overview of the submitted systems 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 before wide-spread commercial deployment on such conversations. © 2024 Elsevier B.V.