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. Current manual methods for measuring narrative coherence are not only difficult to obtain and extremely time consuming but it is unclear whether the human coder can even detect the statistical degree of semantic similarity as the machine can. 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 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, i.e., 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., 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. What this means for people with autism: Potentially significant outcomes include improving the understanding of autism in a way that could lead to better diagnosis, and the refinement of speech analysis technologies that could not only improve the diagnosis of autism, but also make seeking that diagnosis faster and cheaper.