Abstract
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 (HM) of voiced signal, we isolated the harmonic content of speech and computed a set of quantities related to harmonic content. Employing these measures, along with standard speech measures such as loudness, we successfully trained machine learning models for distinguishing individuals with autism from those with typical development (TD). We evaluated our models empirically on a task of detecting autism on a sample of 118 youth (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.
Highlights
Autism spectrum disorder (ASD) comprises a range of developmental impairments affecting social communication and patterns of play and behaviors (American Psychiatric Association, 2013)
The results indicate that prosodic features more accurately distinguished ASD subjects from those with typical development (TD) in comparison to features of articulation due to better specificity of prosody over articulation
In this proof of concept study, we proposed automated methods for characterizing the abnormal prosodic pattern of autism that succeeded in distinguishing subjects with ASD from TD controls
Summary
Autism spectrum disorder (ASD) comprises a range of developmental impairments affecting social communication and patterns of play and behaviors (American Psychiatric Association, 2013). In children with ASD, atypical patterns in prosodic elements such as monotonous pitch (Sharda et al, 2010), reduced stress (Shriberg et al, 2001), odd rhythm (Trevarthen and Daniel, 2005), flat intonation (Cooper and Hanstock, 2009), and even differences in harmonic structure of their speech (Bonneh et al, 2011) are among the earliest signs of the disorder. Speech researchers have proposed automated methods for assessment of prosody (van Santen et al, 2009; Hönig et al, 2010; Truong et al, 2018; Truong et al, 2018) Despite their potential benefits, a major challenge in these systems is the lack of computational algorithms that could extract robust and accurate prosodic measures, such as pitch. By comparing speech samples collected with standardized ADOS2 (Lord et al, 2003) procedures in youth with or without ASD, our study objectives were to: (1) examine if analysis of voice and speech quality only could predict diagnostic membership better than the chance model; and (2) test if speech samples collected in specific ADOS-2 tasks were or not equivalent in differentiating the 2 groups of children and adolescents, and if the combination of speech samples across tasks was improving performance over single tasks samples
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
