MERLIon CCS Challenge
A English-Mandarin code-switching child-directed speech corpus for language identification and diarization
Victoria Y.H. Chua (Nanyang Technological University)
Hexin Liu (Nanyang Technological University)
Leibny Paola Garcia Perera (Johns Hopkins University)
Fei Ting Woon (Nanyang Technological University)
Jinyi Wong (Nanyang Technological University)
Xiangyu Zhang (Johns Hopkins University, TU Delft - Mechanical Engineering)
Sanjeev Khudanpur (Johns Hopkins University)
Andy W.H. Khong (Nanyang Technological University)
Justin Dauwels (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Suzy J. Styles (Nanyang Technological University)
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Abstract
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.