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To enhance the reliability and robustness of language identification (LID)and language diarization (LD) systems for heterogeneous populations andscenarios, there is a need for speech processing models to be trained ondatasets that feature diverse language registers and speech patterns. Wepresent the MERLIon CCS challenge, featuring a first-of-its-kind Zoom videocall dataset of parent-child shared book reading, of over 30 hours with over300 recordings, annotated by multilingual transcribers using a high-fidelitylinguistic transcription protocol. The audio corpus features spontaneous andin-the-wild English-Mandarin code-switching, child-directed speech innon-standard accents with diverse language-mixing patterns recorded in avariety of home environments. This report describes the corpus, as well as LIDand LD results for our baseline and several systems submitted to the MERLIonCCS challenge using the corpus.

MERLIon CCS Challenge: A English-Mandarin code-switching child-directed speech corpus for language identification and diarization