Early Autism Screening via Multimodal Flat Affect Recognition in Voice and Facial Cues

Abstract

Flat affect, characterized by reduced facial and vocal emotional expressiveness, is one of the key indicators of Autism Spectrum Disorder (ASD) in children. The present article suggests a novel automatic system for the early identification of flat affect in 8-11 years old children using a multimodal fusion of facial movement, head pose, global attention, and voice-based emotion features such as pitch, volume, and speech patterns. A decision model integrates all these features to enhance the detection of emotional expressiveness deficits. Compared to previous research on a single modality, this multimodal approach increases detection reliability. Experimental evaluation demonstrates high performance with 90% accuracy, 92.86% precision, and 86.67% recall. The proposed system provides an effective, objective tool to assist in early ASD symptom detection and intervention for psychologists, educators, and parents.