Egocentric Speaker Classification in Child-Adult Dyadic Interactions: From Sensing to Computational Modeling

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by challenges in social communication, repetitive behavior, and sensory processing. One important research area in ASD is evaluating children's behavioral changes over time during treatment. The standard protocol with this objective is BOSCC, which involves dyadic interactions between a child and clinicians performing a pre-defined set of activities. A fundamental aspect of understanding children's behavior in these interactions is automatic speech understanding, particularly identifying who speaks and when. Conventional methods in this area heavily rely on audio recorded from a spectator perspective, and there is limited research on egocentric speech modeling. Here, we experiment with wearable sensors to perform speech sampling in BOSCC interviews from an egocentric perspective. Our findings highlight the potential of egocentric speech collection and pre-training to improve speaker classification accuracy.