This paper introduces the inaugural Multilingual Everyday Recordings- Language Identification on Code-Switched Child-Directed Speech (MERLIon CCS) Challenge, focused on developing robust language identification and language diarization systems that are reliable for non-standard, accented, spontaneous code-switched, child-directed speech collected via Zoom. Aligning closely with Interspeech 2023 theme, the main objectives of this inaugural challenge are to present a unique first-of-its-kind Zoom videocall dataset featuring English-Mandarin spontaneous code-switched child-directed speech, benchmark the current and novel language identification and language diarization systems in a code-switching scenario including extremely short utterances, and test the robustness of such systems under accented speech. The MERLIon CCS challenge features two task: language identification (Task 1) and language diarization (Task 2). Two tracks, open and closed, are available for each task, differing by the volume of data systems can be trained on. This paper describes the dataset, dataset annotation protocol, challenge tasks, open and closed tracks, evaluation metrics, and evaluation protocol.
Cite
@article{arxiv.2305.19493,
title = {MERLIon CCS Challenge Evaluation Plan},
author = {Leibny Paola Garcia Perera and Y. H. Victoria Chua and Hexin Liu and Fei Ting Woon and Andy W. H. Khong and Justin Dauwels and Sanjeev Khudanpur and Suzy J. Styles},
journal= {arXiv preprint arXiv:2305.19493},
year = {2023}
}
Comments
Evaluation plan for Interspeech 2023 special session "MERLIon"