A Voice Recognition Application for the Semantic and Prosodic Analysis of ASD Caregivers

The voice manifests and conveys numerous components of meaning in addition to words, such as prosody and semantics. Previous studies have found that parents of children with Autism Spectrum Disorder (ASD) seem to have a delayed response time compared to parents of children with typical development (TD). Words and number repetitions, duration of pronunciation, and meaning used by parents vary by child diagnosis as well. The aim of this project is to demonstrate that the parent's voice can be a powerful behavioral biomarker in the diagnosis of ASD. Parental quality of life may also be a strong predictor of the quality of life of children with ASD. Given this goal, we propose the creation of a voice analysis application that through Machine Learning (ML) algorithms, is able to detect elements of prosody and semantics for investigation purposes. The application is based on the Autism Diagnostic Interview-Revised (ADI-R) and contains some personality questionnaires. This article focuses on potential voice metrics to extract for in-depth voice analysis. Findings outlined semantic and prosodic metrics that will be implemented in voice recognition analysis of ASD parents. Future studies are expected to recognize that parents of ASD children have distinct differences in prosodic and semantic levels compared with parents of control children. The uniqueness of this study lies in the creation of a tool focused on the voice, through combined ML and psychological techniques. This application has the potential to empower the ADI-R methodology by meeting the terms of validity and objectivity.