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Autistic Traits are Linked to Individual Differences in Familiar Voice Identification / V. G. SKUK in Journal of Autism and Developmental Disorders, 49-7 (July 2019)
[article]
Titre : Autistic Traits are Linked to Individual Differences in Familiar Voice Identification Type de document : Texte imprimé et/ou numérique Auteurs : V. G. SKUK, Auteur ; R. PALERMO, Auteur ; L. BROEMER, Auteur ; S. R. SCHWEINBERGER, Auteur Article en page(s) : p.2747-2767 Langues : Anglais (eng) Mots-clés : Autistic traits Gender differences Individual differences Own-gender-bias. Recognition Voice Index. décimale : PER Périodiques Résumé : Autistic traits vary across the general population, and are linked with face recognition ability. Here we investigated potential links between autistic traits and voice recognition ability for personally familiar voices in a group of 30 listeners (15 female, 16-19 years) from the same local school. Autistic traits (particularly those related to communication and social interaction) were negatively correlated with voice recognition, such that more autistic traits were associated with fewer familiar voices identified and less ability to discriminate familiar from unfamiliar voices. In addition, our results suggest enhanced accessibility of personal semantic information in women compared to men. Overall, this study establishes a detailed pattern of relationships between voice identification performance and autistic traits in the general population. En ligne : http://dx.doi.org/10.1007/s10803-017-3039-y Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=401
in Journal of Autism and Developmental Disorders > 49-7 (July 2019) . - p.2747-2767[article] Autistic Traits are Linked to Individual Differences in Familiar Voice Identification [Texte imprimé et/ou numérique] / V. G. SKUK, Auteur ; R. PALERMO, Auteur ; L. BROEMER, Auteur ; S. R. SCHWEINBERGER, Auteur . - p.2747-2767.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 49-7 (July 2019) . - p.2747-2767
Mots-clés : Autistic traits Gender differences Individual differences Own-gender-bias. Recognition Voice Index. décimale : PER Périodiques Résumé : Autistic traits vary across the general population, and are linked with face recognition ability. Here we investigated potential links between autistic traits and voice recognition ability for personally familiar voices in a group of 30 listeners (15 female, 16-19 years) from the same local school. Autistic traits (particularly those related to communication and social interaction) were negatively correlated with voice recognition, such that more autistic traits were associated with fewer familiar voices identified and less ability to discriminate familiar from unfamiliar voices. In addition, our results suggest enhanced accessibility of personal semantic information in women compared to men. Overall, this study establishes a detailed pattern of relationships between voice identification performance and autistic traits in the general population. En ligne : http://dx.doi.org/10.1007/s10803-017-3039-y Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=401 Combining voice and language features improves automated autism detection / Heather MACFARLANE in Autism Research, 15-7 (July 2022)
[article]
Titre : Combining voice and language features improves automated autism detection Type de document : Texte imprimé et/ou numérique Auteurs : Heather MACFARLANE, Auteur ; Alexandra C. SALEM, Auteur ; Liu CHEN, Auteur ; Meysam ASGARI, Auteur ; Eric FOMBONNE, Auteur Article en page(s) : p.1288-1300 Langues : Anglais (eng) Mots-clés : autism automated measures communication disfluency natural language processing pragmatic language prosody voice Index. décimale : PER Périodiques Résumé : Variability in expressive and receptive language, difficulty with pragmatic language, and prosodic difficulties are all features of autism spectrum disorder (ASD). Quantifying language and voice characteristics is an important step for measuring outcomes for autistic people, yet clinical measurement is cumbersome and costly. Using natural language processing (NLP) methods and a harmonic model of speech, we analyzed language transcripts and audio recordings to automatically classify individuals as ASD or non-ASD. One-hundred fifty-eight participants (88 ASD, 70 non-ASD) ages 7 to 17 were evaluated with the autism diagnostic observation schedule (ADOS-2), module 3. The ADOS-2 was transcribed following modified SALT guidelines. Seven automated language measures (ALMs) and 10 automated voice measures (AVMs) for each participant were generated from the transcripts and audio of one ADOS-2 task. The measures were analyzed using support vector machine (SVM; a binary classifier) and receiver operating characteristic (ROC). The AVM model resulted in an ROC area under the curve (AUC) of 0.7800, the ALM model an AUC of 0.8748, and the combined model a significantly improved AUC of 0.9205. The ALM model better detected ASD participants who were younger and had lower language skills and shorter activity time. ASD participants detected by the AVM model had better language profiles than those detected by the language model. In combination, automated measurement of language and voice characteristics successfully differentiated children with and without autism. This methodology could help design robust outcome measures for future research. LAY SUMMARY: People with autism often struggle with communication differences which traditional clinical measures and language tests cannot fully capture. Using language transcripts and audio recordings from 158 children ages 7 to 17, we showed that automated, objective language and voice measurements successfully predict the child's diagnosis. This methodology could help design improved outcome measures for research. En ligne : http://dx.doi.org/10.1002/aur.2733 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=477
in Autism Research > 15-7 (July 2022) . - p.1288-1300[article] Combining voice and language features improves automated autism detection [Texte imprimé et/ou numérique] / Heather MACFARLANE, Auteur ; Alexandra C. SALEM, Auteur ; Liu CHEN, Auteur ; Meysam ASGARI, Auteur ; Eric FOMBONNE, Auteur . - p.1288-1300.
Langues : Anglais (eng)
in Autism Research > 15-7 (July 2022) . - p.1288-1300
Mots-clés : autism automated measures communication disfluency natural language processing pragmatic language prosody voice Index. décimale : PER Périodiques Résumé : Variability in expressive and receptive language, difficulty with pragmatic language, and prosodic difficulties are all features of autism spectrum disorder (ASD). Quantifying language and voice characteristics is an important step for measuring outcomes for autistic people, yet clinical measurement is cumbersome and costly. Using natural language processing (NLP) methods and a harmonic model of speech, we analyzed language transcripts and audio recordings to automatically classify individuals as ASD or non-ASD. One-hundred fifty-eight participants (88 ASD, 70 non-ASD) ages 7 to 17 were evaluated with the autism diagnostic observation schedule (ADOS-2), module 3. The ADOS-2 was transcribed following modified SALT guidelines. Seven automated language measures (ALMs) and 10 automated voice measures (AVMs) for each participant were generated from the transcripts and audio of one ADOS-2 task. The measures were analyzed using support vector machine (SVM; a binary classifier) and receiver operating characteristic (ROC). The AVM model resulted in an ROC area under the curve (AUC) of 0.7800, the ALM model an AUC of 0.8748, and the combined model a significantly improved AUC of 0.9205. The ALM model better detected ASD participants who were younger and had lower language skills and shorter activity time. ASD participants detected by the AVM model had better language profiles than those detected by the language model. In combination, automated measurement of language and voice characteristics successfully differentiated children with and without autism. This methodology could help design robust outcome measures for future research. LAY SUMMARY: People with autism often struggle with communication differences which traditional clinical measures and language tests cannot fully capture. Using language transcripts and audio recordings from 158 children ages 7 to 17, we showed that automated, objective language and voice measurements successfully predict the child's diagnosis. This methodology could help design improved outcome measures for research. En ligne : http://dx.doi.org/10.1002/aur.2733 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=477 “Is voice a marker for Autism spectrum disorder? A systematic review and meta-analysis” / Riccardo FUSAROLI in Autism Research, 10-3 (March 2017)
[article]
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
in Autism Research > 10-3 (March 2017) . - p.384-407[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 Nasal voice in boys with high-functioning autism spectrum disorder / Audrey M. SMERBECK in Research in Autism Spectrum Disorders, 17 (September 2015)
[article]
Titre : Nasal voice in boys with high-functioning autism spectrum disorder Type de document : Texte imprimé et/ou numérique Auteurs : Audrey M. SMERBECK, Auteur Année de publication : 2015 Article en page(s) : p.116-125 Langues : Anglais (eng) Mots-clés : Autism spectrum disorder HFASD Asperger's disorder Nasality Voice Resonance Index. décimale : PER Périodiques Résumé : Abstract This study compared speech samples of 29 boys aged 6–13 with high-functioning autism spectrum disorder (HFASD) to those of 29 typically developing (TD) boys matched on age and ethnicity. Ten listeners blind to speakers’ diagnoses rated speech samples for nasality and reported their perceptions of the speaker on a 6-point Likert-type scale. Results indicated significantly greater listener-perceived nasality in the HFASD than the TD group. Listeners rated the HFASD group significantly higher than the TD group on negative socially relevant adjectives, a finding which was mediated by nasality. In addition, compared to TD speakers, speakers with HFASD were rated lower on dominance and perceived age, as well as higher on perceived disability. En ligne : http://dx.doi.org/10.1016/j.rasd.2015.06.009 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=263
in Research in Autism Spectrum Disorders > 17 (September 2015) . - p.116-125[article] Nasal voice in boys with high-functioning autism spectrum disorder [Texte imprimé et/ou numérique] / Audrey M. SMERBECK, Auteur . - 2015 . - p.116-125.
Langues : Anglais (eng)
in Research in Autism Spectrum Disorders > 17 (September 2015) . - p.116-125
Mots-clés : Autism spectrum disorder HFASD Asperger's disorder Nasality Voice Resonance Index. décimale : PER Périodiques Résumé : Abstract This study compared speech samples of 29 boys aged 6–13 with high-functioning autism spectrum disorder (HFASD) to those of 29 typically developing (TD) boys matched on age and ethnicity. Ten listeners blind to speakers’ diagnoses rated speech samples for nasality and reported their perceptions of the speaker on a 6-point Likert-type scale. Results indicated significantly greater listener-perceived nasality in the HFASD than the TD group. Listeners rated the HFASD group significantly higher than the TD group on negative socially relevant adjectives, a finding which was mediated by nasality. In addition, compared to TD speakers, speakers with HFASD were rated lower on dominance and perceived age, as well as higher on perceived disability. En ligne : http://dx.doi.org/10.1016/j.rasd.2015.06.009 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=263 Psychometric properties of the Cambridge-Mindreading Face-Voice Battery for Children in children with ASD / Jonathan D. RODGERS in Autism Research, 14-9 (September 2021)
[article]
Titre : Psychometric properties of the Cambridge-Mindreading Face-Voice Battery for Children in children with ASD Type de document : Texte imprimé et/ou numérique Auteurs : Jonathan D. RODGERS, Auteur ; C. LOPATA, Auteur ; Adam J. BOOTH, Auteur ; M. L. THOMEER, Auteur ; James P. DONNELLY, Auteur ; C. J. RAJNISZ, Auteur ; J. T. WOOD, Auteur ; J. LODI-SMITH, Auteur ; K. F. KOZLOWSKI, Auteur Article en page(s) : p.1965-1974 Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder Child Emotions Facial Expression Facial Recognition Humans Psychometrics Reproducibility of Results Voice Cambridge-Mindreading Face-Voice Battery for Children children facial emotion recognition psychometrics social cognition vocal emotion recognition Index. décimale : PER Périodiques Résumé : This study examined the psychometric characteristics of the Cambridge-Mindreading Face-Voice Battery for Children (CAM-C) for a sample of 333 children, ages 6-12?years with ASD (with no intellectual disability). Internal consistency was very good for the Total score (0.81 for both Faces and Voices) and respectable for the Complex emotions score (0.72 for Faces and 0.74 for Voices); however, internal consistency was lower for Simple emotions (0.65 for Faces and 0.61 for Voices). Test-retest reliability at 18 and 36?weeks was very good for the faces and voices total (0.76-0.81) and good for simple and complex faces and voices (0.53-0.75). Significant correlations were found between CAM-C Faces and scores on another measure of face-emotion recognition (Diagnostic Analysis of Nonverbal Accuracy-Second Edition), and between Faces and Voices scores and child age, IQ (except perceptual IQ and Simple Voice emotions), and language ability. Parent-reported ASD symptom severity and the Emotion Recognition scale on the SRS-2 were not related to CAM-C scores. Suggestions for future studies and further development of the CAM-C are provided. LAY SUMMARY: Facial and vocal emotion recognition are important for social interaction and have been identified as a challenge for individuals with autism spectrum disorder. Emotion recognition is an area frequently targeted by interventions. This study evaluated a measure of emotion recognition (the CAM-C) for its consistency and validity in a large sample of children with autism. The study found the CAM-C showed many strengths needed to accurately measure the change in emotion recognition during intervention. En ligne : http://dx.doi.org/10.1002/aur.2546 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=450
in Autism Research > 14-9 (September 2021) . - p.1965-1974[article] Psychometric properties of the Cambridge-Mindreading Face-Voice Battery for Children in children with ASD [Texte imprimé et/ou numérique] / Jonathan D. RODGERS, Auteur ; C. LOPATA, Auteur ; Adam J. BOOTH, Auteur ; M. L. THOMEER, Auteur ; James P. DONNELLY, Auteur ; C. J. RAJNISZ, Auteur ; J. T. WOOD, Auteur ; J. LODI-SMITH, Auteur ; K. F. KOZLOWSKI, Auteur . - p.1965-1974.
Langues : Anglais (eng)
in Autism Research > 14-9 (September 2021) . - p.1965-1974
Mots-clés : Autism Spectrum Disorder Child Emotions Facial Expression Facial Recognition Humans Psychometrics Reproducibility of Results Voice Cambridge-Mindreading Face-Voice Battery for Children children facial emotion recognition psychometrics social cognition vocal emotion recognition Index. décimale : PER Périodiques Résumé : This study examined the psychometric characteristics of the Cambridge-Mindreading Face-Voice Battery for Children (CAM-C) for a sample of 333 children, ages 6-12?years with ASD (with no intellectual disability). Internal consistency was very good for the Total score (0.81 for both Faces and Voices) and respectable for the Complex emotions score (0.72 for Faces and 0.74 for Voices); however, internal consistency was lower for Simple emotions (0.65 for Faces and 0.61 for Voices). Test-retest reliability at 18 and 36?weeks was very good for the faces and voices total (0.76-0.81) and good for simple and complex faces and voices (0.53-0.75). Significant correlations were found between CAM-C Faces and scores on another measure of face-emotion recognition (Diagnostic Analysis of Nonverbal Accuracy-Second Edition), and between Faces and Voices scores and child age, IQ (except perceptual IQ and Simple Voice emotions), and language ability. Parent-reported ASD symptom severity and the Emotion Recognition scale on the SRS-2 were not related to CAM-C scores. Suggestions for future studies and further development of the CAM-C are provided. LAY SUMMARY: Facial and vocal emotion recognition are important for social interaction and have been identified as a challenge for individuals with autism spectrum disorder. Emotion recognition is an area frequently targeted by interventions. This study evaluated a measure of emotion recognition (the CAM-C) for its consistency and validity in a large sample of children with autism. The study found the CAM-C showed many strengths needed to accurately measure the change in emotion recognition during intervention. En ligne : http://dx.doi.org/10.1002/aur.2546 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=450 Atypical sound discrimination in children with ASD as indicated by cortical ERPs / Aurélie BIDET-CAULET in Journal of Neurodevelopmental Disorders, 9-1 (December 2017)
PermalinkAuditory Attention Deployment in Young Adults with Autism Spectrum Disorder / Katherine A. EMMONS in Journal of Autism and Developmental Disorders, 52-4 (April 2022)
PermalinkToward 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)
PermalinkVocal markers of autism: Assessing the generalizability of machine learning models / Astrid RYBNER in Autism Research, 15-6 (June 2022)
PermalinkEnhanced Memory for Vocal Melodies in Autism Spectrum Disorder and Williams Syndrome / M. W. WEISS in Autism Research, 14-6 (June 2021)
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