  Speech foundation models, trained on vast datasets, have opened unique
opportunities in addressing challenging low-resource speech understanding, such
as child speech. In this work, we explore the capabilities of speech foundation
models on child-adult speaker diarization. We show that exemplary foundation
models can achieve 39.5% and 62.3% relative reductions in Diarization Error
Rate and Speaker Confusion Rate, respectively, compared to previous speaker
diarization methods. In addition, we benchmark and evaluate the speaker
diarization results of the speech foundation models with varying the input
audio window size, speaker demographics, and training data ratio. Our results
highlight promising pathways for understanding and adopting speech foundation
models to facilitate child speech understanding.
