  To enhance the reliability and robustness of language identification (LID)
and language diarization (LD) systems for heterogeneous populations and
scenarios, there is a need for speech processing models to be trained on
datasets that feature diverse language registers and speech patterns. We
present the MERLIon CCS challenge, featuring a first-of-its-kind Zoom video
call dataset of parent-child shared book reading, of over 30 hours with over
300 recordings, annotated by multilingual transcribers using a high-fidelity
linguistic transcription protocol. The audio corpus features spontaneous and
in-the-wild English-Mandarin code-switching, child-directed speech in
non-standard accents with diverse language-mixing patterns recorded in a
variety of home environments. This report describes the corpus, as well as LID
and LD results for our baseline and several systems submitted to the MERLIon
CCS challenge using the corpus.
