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Is voice a marker for Autism spectrum disorder? A systematic review and meta-analysis

Journal

AUTISM RESEARCH
Volume 10, Issue 3, Pages 384-407

Publisher

WILEY
DOI: 10.1002/aur.1678

Keywords

voice; speech; acoustic properties; machine learning; biomarker

Funding

  1. Seed Funding Program of The Interacting Minds Center
  2. Calleva Research Centre for Evolution and Human Sciences

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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 Res2017, 10: 384-407. (c) 2016 International Society for Autism Research, Wiley Periodicals, Inc.

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