  Autism Spectrum Disorders (ASD) describe a heterogeneous set of conditions
classified as neurodevelopmental disorders. Although the mechanisms underlying
ASD are not yet fully understood, more recent literature focused on multiple
genetics and/or environmental risk factors. Heterogeneity of symptoms,
especially in milder forms of this condition, could be a challenge for the
clinician. In this work, an automatic speech classification algorithm is
proposed to characterize the prosodic elements that best distinguish autism, to
support the traditional diagnosis. The performance of the proposed algorithm is
evaluted by testing the classification algorithms on a dataset composed of
recorded speeches, collected among both autustic and non autistic subjects.
