“Is voice a marker for Autism spectrum disorder? A systematic review and meta-analysis” / Riccardo FUSAROLI in Autism Research, 10-3 (March 2017)  

Public ISBD [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

Toward a cumulative science of vocal markers of autism: A cross-linguistic meta-analysis-based investigation of acoustic markers in American and Danish autistic children / Riccardo FUSAROLI in Autism Research, 15-4 (April 2022)  

Public ISBD [article]  inAutism Research > 15-4 (April 2022) . - p.653-664 Titre : Toward a cumulative science of vocal markers of autism: A cross-linguistic meta-analysis-based investigation of acoustic markers in American and Danish autistic children Type de document : Texte imprimé et/ou numérique Auteurs : Riccardo FUSAROLI, Auteur ; Ruth GROSSMAN, Auteur ; Niels BILENBERG, Auteur ; Cathriona CANTIO, Auteur ; Jens Richardt MØLLEGAARD JEPSEN, Auteur ; Ethan WEED, Auteur Article en page(s) : p.653-664 Langues : Anglais (eng) Mots-clés : Acoustics  Adolescent  Autism Spectrum Disorder/diagnosis  Autistic Disorder  Biomarkers  Child  Denmark  Humans  Language  Linguistics  autism spectrum disorder  cross-linguistic  speech  voice Index. décimale : PER Périodiques Résumé : Acoustic atypicalities in speech production are argued to be potential markers of clinical features in autism spectrum disorder (ASD). A recent meta-analysis highlighted shortcomings in the field, in particular small sample sizes and study heterogeneity. We showcase a cumulative (i.e., explicitly building on previous studies both conceptually and statistically) yet self-correcting (i.e., critically assessing the impact of cumulative statistical techniques) approach to prosody in ASD to overcome these issues. We relied on the recommendations contained in the meta-analysis to build and analyze a cross-linguistic corpus of multiple speech productions in 77 autistic and 72 neurotypical children and adolescents (>1000 recordings in Danish and US English). We used meta-analytically informed and skeptical priors, with informed priors leading to more generalizable inference. We replicated findings of a minimal cross-linguistically reliable distinctive acoustic profile for ASD (higher pitch and longer pauses) with moderate effect sizes. We identified novel reliable differences between the two groups for normalized amplitude quotient, maxima dispersion quotient, and creakiness. However, the differences were small, and there is likely no one acoustic profile characterizing all autistic individuals. We identified reliable relations of acoustic features with individual differences (age, gender), and clinical features (speech rate and ADOS sub-scores). Besides cumulatively building our understanding of acoustic atypicalities in ASD, the study shows how to use systematic reviews and meta-analyses to guide the design and analysis of follow-up studies. We indicate future directions: larger and more diverse cross-linguistic datasets, focus on heterogeneity, self-critical cumulative approaches, and open science. LAY SUMMARY: Autistic individuals are reported to speak in distinctive ways. Distinctive vocal production can affect social interactions and social development and could represent a noninvasive way to support the assessment of autism spectrum disorder (ASD). We systematically checked whether acoustic atypicalities highlighted in previous articles could be actually found across multiple recordings and two languages. We find a minimal acoustic profile of ASD: higher pitch, longer pauses, increased hoarseness and creakiness of the voice. However, there is much individual variability (by age, sex, language, and clinical characteristics). This suggests that the search for one common "autistic voice" might be naive and more fine-grained approaches are needed. En ligne : https://dx.doi.org/10.1002/aur.2661 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=473 [article] Toward a cumulative science of vocal markers of autism: A cross-linguistic meta-analysis-based investigation of acoustic markers in American and Danish autistic children [Texte imprimé et/ou numérique] / Riccardo FUSAROLI, Auteur ; Ruth GROSSMAN, Auteur ; Niels BILENBERG, Auteur ; Cathriona CANTIO, Auteur ; Jens Richardt MØLLEGAARD JEPSEN, Auteur ; Ethan WEED, Auteur . - p.653-664. Langues : Anglais (eng) in Autism Research > 15-4 (April 2022) . - p.653-664 Mots-clés : Acoustics  Adolescent  Autism Spectrum Disorder/diagnosis  Autistic Disorder  Biomarkers  Child  Denmark  Humans  Language  Linguistics  autism spectrum disorder  cross-linguistic  speech  voice Index. décimale : PER Périodiques Résumé : Acoustic atypicalities in speech production are argued to be potential markers of clinical features in autism spectrum disorder (ASD). A recent meta-analysis highlighted shortcomings in the field, in particular small sample sizes and study heterogeneity. We showcase a cumulative (i.e., explicitly building on previous studies both conceptually and statistically) yet self-correcting (i.e., critically assessing the impact of cumulative statistical techniques) approach to prosody in ASD to overcome these issues. We relied on the recommendations contained in the meta-analysis to build and analyze a cross-linguistic corpus of multiple speech productions in 77 autistic and 72 neurotypical children and adolescents (>1000 recordings in Danish and US English). We used meta-analytically informed and skeptical priors, with informed priors leading to more generalizable inference. We replicated findings of a minimal cross-linguistically reliable distinctive acoustic profile for ASD (higher pitch and longer pauses) with moderate effect sizes. We identified novel reliable differences between the two groups for normalized amplitude quotient, maxima dispersion quotient, and creakiness. However, the differences were small, and there is likely no one acoustic profile characterizing all autistic individuals. We identified reliable relations of acoustic features with individual differences (age, gender), and clinical features (speech rate and ADOS sub-scores). Besides cumulatively building our understanding of acoustic atypicalities in ASD, the study shows how to use systematic reviews and meta-analyses to guide the design and analysis of follow-up studies. We indicate future directions: larger and more diverse cross-linguistic datasets, focus on heterogeneity, self-critical cumulative approaches, and open science. LAY SUMMARY: Autistic individuals are reported to speak in distinctive ways. Distinctive vocal production can affect social interactions and social development and could represent a noninvasive way to support the assessment of autism spectrum disorder (ASD). We systematically checked whether acoustic atypicalities highlighted in previous articles could be actually found across multiple recordings and two languages. We find a minimal acoustic profile of ASD: higher pitch, longer pauses, increased hoarseness and creakiness of the voice. However, there is much individual variability (by age, sex, language, and clinical characteristics). This suggests that the search for one common "autistic voice" might be naive and more fine-grained approaches are needed. En ligne : https://dx.doi.org/10.1002/aur.2661 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=473

Vocal markers of autism: Assessing the generalizability of machine learning models / Astrid RYBNER in Autism Research, 15-6 (June 2022)  

Public ISBD [article]  inAutism Research > 15-6 (June 2022) . - p.1018-1030 Titre : Vocal markers of autism: Assessing the generalizability of machine learning models Type de document : Texte imprimé et/ou numérique Auteurs : Astrid RYBNER, Auteur ; Emil TRENCKNER JESSEN, Auteur ; Marie DAMSGAARD MORTENSEN, Auteur ; Stine Nyhus LARSEN, Auteur ; Ruth GROSSMAN, Auteur ; Niels BILENBERG, Auteur ; Cathriona CANTIO, Auteur ; Jens Richardt MØLLEGAARD JEPSEN, Auteur ; Ethan WEED, Auteur ; Arndis SIMONSEN, Auteur ; Riccardo FUSAROLI, Auteur Article en page(s) : p.1018-1030 Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder  Autistic Disorder/diagnosis  Biomarkers  Humans  Machine Learning  Speech  Voice  biobehavioral markers  generalizability Index. décimale : PER Périodiques Résumé : Machine learning (ML) approaches show increasing promise in their ability to identify vocal markers of autism. Nonetheless, it is unclear to what extent such markers generalize to new speech samples collected, for example, using a different speech task or in a different language. In this paper, we systematically assess the generalizability of ML findings across a variety of contexts. We train promising published ML models of vocal markers of autism on novel cross-linguistic datasets following a rigorous pipeline to minimize overfitting, including cross-validated training and ensemble models. We test the generalizability of the models by testing them on (i) different participants from the same study, performing the same task; (ii) the same participants, performing a different (but similar) task; (iii) a different study with participants speaking a different language, performing the same type of task. While model performance is similar to previously published findings when trained and tested on data from the same study (out-of-sample performance), there is considerable variance between studies. Crucially, the models do not generalize well to different, though similar, tasks and not at all to new languages. The ML pipeline is openly shared. Generalizability of ML models of vocal markers of autism is an issue. We outline three recommendations for strategies researchers could take to be more explicit about generalizability and improve it in future studies. LAY SUMMARY: Machine learning approaches promise to be able to identify autism from voice only. These models underestimate how diverse the contexts in which we speak are, how diverse the languages used are and how diverse autistic voices are. Machine learning approaches need to be more careful in defining their limits and generalizability. En ligne : http://dx.doi.org/10.1002/aur.2721 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=476 [article] Vocal markers of autism: Assessing the generalizability of machine learning models [Texte imprimé et/ou numérique] / Astrid RYBNER, Auteur ; Emil TRENCKNER JESSEN, Auteur ; Marie DAMSGAARD MORTENSEN, Auteur ; Stine Nyhus LARSEN, Auteur ; Ruth GROSSMAN, Auteur ; Niels BILENBERG, Auteur ; Cathriona CANTIO, Auteur ; Jens Richardt MØLLEGAARD JEPSEN, Auteur ; Ethan WEED, Auteur ; Arndis SIMONSEN, Auteur ; Riccardo FUSAROLI, Auteur . - p.1018-1030. Langues : Anglais (eng) in Autism Research > 15-6 (June 2022) . - p.1018-1030 Mots-clés : Autism Spectrum Disorder  Autistic Disorder/diagnosis  Biomarkers  Humans  Machine Learning  Speech  Voice  biobehavioral markers  generalizability Index. décimale : PER Périodiques Résumé : Machine learning (ML) approaches show increasing promise in their ability to identify vocal markers of autism. Nonetheless, it is unclear to what extent such markers generalize to new speech samples collected, for example, using a different speech task or in a different language. In this paper, we systematically assess the generalizability of ML findings across a variety of contexts. We train promising published ML models of vocal markers of autism on novel cross-linguistic datasets following a rigorous pipeline to minimize overfitting, including cross-validated training and ensemble models. We test the generalizability of the models by testing them on (i) different participants from the same study, performing the same task; (ii) the same participants, performing a different (but similar) task; (iii) a different study with participants speaking a different language, performing the same type of task. While model performance is similar to previously published findings when trained and tested on data from the same study (out-of-sample performance), there is considerable variance between studies. Crucially, the models do not generalize well to different, though similar, tasks and not at all to new languages. The ML pipeline is openly shared. Generalizability of ML models of vocal markers of autism is an issue. We outline three recommendations for strategies researchers could take to be more explicit about generalizability and improve it in future studies. LAY SUMMARY: Machine learning approaches promise to be able to identify autism from voice only. These models underestimate how diverse the contexts in which we speak are, how diverse the languages used are and how diverse autistic voices are. Machine learning approaches need to be more careful in defining their limits and generalizability. En ligne : http://dx.doi.org/10.1002/aur.2721 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=476

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