This project seeks to bring the power of machine-based sensing and computation to improve the study of speech patterns in individuals with autism. By combining technologies stemming from natural language processing methods and prosodic analysis methods, they expect to find aspects of speech that could be used as clinical markers. This research will analyze recordings being collected from two narrative recall tests that have the potential to uncover a wider range of speech differences between autism spectrum disorders (ASD) and others. The hope is that this will clinically define children with ASD relative to typically-developing children and differentiate ASD from other groups who also have communication impairments, e.g. children with developmental language delay (DLD) as well as differentiate speech characteristics or markers that might better discriminate subtypes within the ASD umbrella (e.g., high-functioning autism (HFA) vs. Asperger's). They expect that speech and language technologies will not only make critical diagnostic speech features easier to document but also may actually uncover distinguishing speech features in autism and autistic subtypes that have previously gone undetected.