[article] 
inAutism Research > 10-3 (March 2017) . - p.384-407

Titre :	“Is voice a marker for Autism spectrum disorder? A systematic review and meta-analysis”
Type de document :	Texte imprimé et/ou numérique
Auteurs :	Riccardo FUSAROLI, Auteur ; Anna LAMBRECHTS, Auteur ; Dan BANG, Auteur ; Dermot M. BOWLER, Auteur ; Sebastian B. GAIGG, Auteur
Article en page(s) :	p.384-407
Langues :	Anglais (eng)
Mots-clés :	voice  speech  acoustic properties  machine learning  biomarker
Index. décimale :	PER Périodiques
Résumé :	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.
En ligne :	http://dx.doi.org/10.1002/aur.1678
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=304

[article] “Is voice a marker for Autism spectrum disorder? A systematic review and meta-analysis” [Texte imprimé et/ou numérique] / Riccardo FUSAROLI, Auteur ; Anna LAMBRECHTS, Auteur ; Dan BANG, Auteur ; Dermot M. BOWLER, Auteur ; Sebastian B. GAIGG, Auteur . - p.384-407.
Langues : Anglais (eng)
in Autism Research > 10-3 (March 2017) . - p.384-407

Mots-clés :	voice  speech  acoustic properties  machine learning  biomarker
Index. décimale :	PER Périodiques
Résumé :	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.
En ligne :	http://dx.doi.org/10.1002/aur.1678
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=304