| Literature DB >> 27501063 |
Riccardo Fusaroli1, Anna Lambrechts2, Dan Bang1,3,4, Dermot M Bowler2, Sebastian B Gaigg2.
Abstract
Individuals with Autism Spectrum Disorder (ASD) tend to show distinctive, atypical acoustic patterns of speech. These behaviors affect social interactions and social development and could represent a non-invasive marker for ASD. We systematically reviewed the literature quantifying acoustic patterns in ASD. Search terms were: (prosody OR intonation OR inflection OR intensity OR pitch OR fundamental frequency OR speech rate OR voice quality OR acoustic) AND (autis* OR Asperger). Results were filtered to include only: empirical studies quantifying acoustic features of vocal production in ASD, with a sample size >2, and the inclusion of a neurotypical comparison group and/or correlations between acoustic measures and severity of clinical features. We identified 34 articles, including 30 univariate studies and 15 multivariate machine-learning studies. We performed meta-analyses of the univariate studies, identifying significant differences in mean pitch and pitch range between individuals with ASD and comparison participants (Cohen's d of 0.4-0.5 and discriminatory accuracy of about 61-64%). The multivariate studies reported higher accuracies than the univariate studies (63-96%). However, the methods used and the acoustic features investigated were too diverse for performing meta-analysis. We conclude that multivariate studies of acoustic patterns are a promising but yet unsystematic avenue for establishing ASD markers. We outline three recommendations for future studies: open data, open methods, and theory-driven research. Autism Res 2017, 10: 384-407.Entities:
Keywords: acoustic properties; biomarker; machine learning; speech; voice
Mesh:
Year: 2016 PMID: 27501063 DOI: 10.1002/aur.1678
Source DB: PubMed Journal: Autism Res ISSN: 1939-3806 Impact factor: 5.216