  We address the problem of detecting who spoke when in child-inclusive spoken
interactions i.e., automatic child-adult speaker classification. Interactions
involving children are richly heterogeneous due to developmental differences.
The presence of neurodiversity e.g., due to Autism, contributes additional
variability. We investigate the impact of additional pre-training with more
unlabelled child speech on the child-adult classification performance. We
pre-train our model with child-inclusive interactions, following two recent
self-supervision algorithms, Wav2vec 2.0 and WavLM, with a contrastive loss
objective. We report 9 - 13% relative improvement over the state-of-the-art
baseline with regards to classification F1 scores on two clinical interaction
datasets involving children with Autism. We also analyze the impact of
pre-training under different conditions by evaluating our model on interactions
involving different subgroups of children based on various demographic factors.
