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.

翻译：自闭症谱系障碍（ASD）描述了一组被归类为神经发育障碍的异质性病症。尽管ASD的潜在机制尚未完全阐明，但近期的文献更多地关注多种遗传和/或环境风险因素。症状的异质性，尤其是在该病症的较轻微形式中，可能对临床医生构成挑战。本研究提出了一种自动语音分类算法，旨在刻画最能区分自闭症的韵律特征，以辅助传统诊断。通过在一个由自闭症和非自闭症受试者的录音语音组成的数据集上测试分类算法，对所提算法的性能进行了评估。