  Interactions involving children span a wide range of important domains from
learning to clinical diagnostic and therapeutic contexts. Automated analyses of
such interactions are motivated by the need to seek accurate insights and offer
scale and robustness across diverse and wide-ranging conditions. Identifying
the speech segments belonging to the child is a critical step in such modeling.
Conventional child-adult speaker classification typically relies on audio
modeling approaches, overlooking visual signals that convey speech articulation
information, such as lip motion. Building on the foundation of an audio-only
child-adult speaker classification pipeline, we propose incorporating visual
cues through active speaker detection and visual processing models. Our
framework involves video pre-processing, utterance-level child-adult speaker
detection, and late fusion of modality-specific predictions. We demonstrate
from extensive experiments that a visually aided classification pipeline
enhances the accuracy and robustness of the classification. We show relative
improvements of 2.38% and 3.97% in F1 macro score when one face and two faces
are visible, respectively.
