Quantifying Voice Characteristics for Detecting Autism - Asgari, Meysam; Chen, Liu; Fombonne, Eric (Frontiers in Psychology, 2021)
The presence of prosodic anomalies in autistic is recognized by experienced clinicians but their quantitative analysis is a cumbersome task beyond the scope of typical pen and pencil assessment. This paper proposes an automatic approach allowing to tease apart various aspects of prosodic abnormalities and to translate them into fine-grained, automated, and quantifiable measurements. Using a harmonic model of voiced signal, we isolated the harmonic content of speech and computed a set of quantities related to harmonic con- tent. Employing these measures, along with standard speech measures such as loudness, we successfully trained machine learning models for distinguishing children with autism from those with typical development. We evaluated our models empirically on a task of detecting autism on a sample of 118 children (90 diagnosed with autism and 28 controls; mean age: 10.9 years) and demonstrated that these models perform significantly better than a chance model. Voice and speech analyses could be incorporated as novel outcome measures for treatment research and used for early detection of autism in preverbal infants or toddlers at risk of autism.