Abstract:
Verbal speech of children diagnosed with ASD is explored in order to identify patterns autism has left in speech, and to model such patterns for implementing automatic di...Show MoreMetadata
Abstract:
Verbal speech of children diagnosed with ASD is explored in order to identify patterns autism has left in speech, and to model such patterns for implementing automatic diagnostic and screening frameworks. In this study, we identify the deviations of acoustic low-level descriptors (LLDs) in voice of an autistic adolescent from her typically developing triplet siblings. The goal is to identify the atypicality in voice introduced by autism under minimum gender, age, genetic, and language bias and use the gained insights to build a more generalized model by adding more subjects hierarchically. We report the most significant LLDs that describe the deviations of acoustic features due to autism for categories of utterances and feature groups.
Published in: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Date of Conference: 18-21 July 2018
Date Added to IEEE Xplore: 28 October 2018
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PubMed ID: 30441227