Explainable Children Autism Detection using Gaze Features in Audio-Visual Speech Comprehension ETRA012

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition marked by impairments in social interaction and delayed language acquisition. Early and accurate identification is crucial for timely interventions that support cognitive and social development. Motivated by the subjectivity of traditional behavior-based assessments, computational methodologies offer more objective and cost-effective alternatives. Among these, eye-tracking stands out for capturing subtle attentional and perceptual patterns. This paper investigates the use of eye-tracking data for automatic ASD detection in children during audio-visual storytelling interactions, emphasizing traditional yet explainable machine learning methods. Although performance remains modest, our analyses reveal that fixation duration and revisit patterns to facial regions may serve as potential biomarkers. Further analyses highlight the impact of stimulus modality, suggesting that the inclusion of visual speech cues provides valuable discriminative information. These findings have the potential to support and guide the work of psychologists in the assessment of ASD within speech comprehension contexts.