  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.
