Combining voice and language features improves automated autism detection / Heather MACFARLANE in Autism Research, 15-7 (July 2022)  

Public ISBD [article]  inAutism Research > 15-7 (July 2022) . - p.1288-1300 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 [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

Consistency and reliability of automated language measures across expressive language samples in autism / Heather MACFARLANE in Autism Research, 16-4 (April 2023)  

Public ISBD [article]  inAutism Research > 16-4 (April 2023) . - p.802-816 Titre : Consistency and reliability of automated language measures across expressive language samples in autism Type de document : Texte imprimé et/ou numérique Auteurs : Heather MACFARLANE, Auteur ; Alexandra C. SALEM, Auteur ; Steven BEDRICK, Auteur ; Jill K. DOLATA, Auteur ; Jack WIEDRICK, Auteur ; Grace O. LAWLEY, Auteur ; Lizbeth H. FINESTACK, Auteur ; Sara T. KOVER, Auteur ; Angela John THURMAN, Auteur ; Leonard ABBEDUTO, Auteur ; Eric FOMBONNE, Auteur Article en page(s) : p.802-816 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with substantial clinical heterogeneity, especially in language and communication ability. There is a need for validated language outcome measures that show sensitivity to true change for this population. We used Natural Language Processing to analyze expressive language transcripts of 64 highly-verbal children and young adults (age: 6-23?years, mean 12.8?years; 78.1% male) with ASD to examine the validity across language sampling context and test-retest reliability of six previously validated Automated Language Measures (ALMs), including Mean Length of Utterance in Morphemes, Number of Distinct Word Roots, C-units per minute, unintelligible proportion, um rate, and repetition proportion. Three expressive language samples were collected at baseline and again 4?weeks later. These samples comprised interview tasks from the Autism Diagnostic Observation Schedule (ADOS-2) Modules 3 and 4, a conversation task, and a narration task. The influence of language sampling context on each ALM was estimated using either generalized linear mixed-effects models or generalized linear models, adjusted for age, sex, and IQ. The 4?weeks test-retest reliability was evaluated using Lin's Concordance Correlation Coefficient (CCC). The three different sampling contexts were associated with significantly (P En ligne : https://doi.org/10.1002/aur.2897 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=499 [article] Consistency and reliability of automated language measures across expressive language samples in autism [Texte imprimé et/ou numérique] / Heather MACFARLANE, Auteur ; Alexandra C. SALEM, Auteur ; Steven BEDRICK, Auteur ; Jill K. DOLATA, Auteur ; Jack WIEDRICK, Auteur ; Grace O. LAWLEY, Auteur ; Lizbeth H. FINESTACK, Auteur ; Sara T. KOVER, Auteur ; Angela John THURMAN, Auteur ; Leonard ABBEDUTO, Auteur ; Eric FOMBONNE, Auteur . - p.802-816. Langues : Anglais (eng) in Autism Research > 16-4 (April 2023) . - p.802-816 Index. décimale : PER Périodiques Résumé : Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with substantial clinical heterogeneity, especially in language and communication ability. There is a need for validated language outcome measures that show sensitivity to true change for this population. We used Natural Language Processing to analyze expressive language transcripts of 64 highly-verbal children and young adults (age: 6-23?years, mean 12.8?years; 78.1% male) with ASD to examine the validity across language sampling context and test-retest reliability of six previously validated Automated Language Measures (ALMs), including Mean Length of Utterance in Morphemes, Number of Distinct Word Roots, C-units per minute, unintelligible proportion, um rate, and repetition proportion. Three expressive language samples were collected at baseline and again 4?weeks later. These samples comprised interview tasks from the Autism Diagnostic Observation Schedule (ADOS-2) Modules 3 and 4, a conversation task, and a narration task. The influence of language sampling context on each ALM was estimated using either generalized linear mixed-effects models or generalized linear models, adjusted for age, sex, and IQ. The 4?weeks test-retest reliability was evaluated using Lin's Concordance Correlation Coefficient (CCC). The three different sampling contexts were associated with significantly (P En ligne : https://doi.org/10.1002/aur.2897 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=499

"Um" and "Uh" Usage Patterns in Children with Autism: Associations with Measures of Structural and Pragmatic Language Ability / Grace O. LAWLEY in Journal of Autism and Developmental Disorders, 53-8 (August 2023)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 53-8 (August 2023) . - p.2986-2997 Titre : "Um" and "Uh" Usage Patterns in Children with Autism: Associations with Measures of Structural and Pragmatic Language Ability Type de document : Texte imprimé et/ou numérique Auteurs : Grace O. LAWLEY, Auteur ; Steven BEDRICK, Auteur ; Heather MACFARLANE, Auteur ; Jill K. DOLATA, Auteur ; Alexandra C. SALEM, Auteur ; Eric FOMBONNE, Auteur Article en page(s) : p.2986-2997 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Pragmatic language difficulties, including unusual filler usage, are common among children with Autism Spectrum Disorder (ASD). This study investigated "um" and "uh" usage in children with ASD and typically developing (TD) controls. We analyzed transcribed Autism Diagnostic Observation Schedule (ADOS) sessions for 182 children (117 ASD, 65 TD), aged 4 to 15. Although the groups did not differ in "uh" usage, the ASD group used fewer "ums" than the TD group. This held true after controlling for age, sex, and IQ. Within ASD, social affect and pragmatic language scores did not predict filler usage; however, structural language scores predicted "um" usage. Lower "um" rates among children with ASD may reflect problems with planning or production rather than pragmatic language. En ligne : https://doi.org/10.1007/s10803-022-05565-4 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=508 [article] "Um" and "Uh" Usage Patterns in Children with Autism: Associations with Measures of Structural and Pragmatic Language Ability [Texte imprimé et/ou numérique] / Grace O. LAWLEY, Auteur ; Steven BEDRICK, Auteur ; Heather MACFARLANE, Auteur ; Jill K. DOLATA, Auteur ; Alexandra C. SALEM, Auteur ; Eric FOMBONNE, Auteur . - p.2986-2997. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 53-8 (August 2023) . - p.2986-2997 Index. décimale : PER Périodiques Résumé : Pragmatic language difficulties, including unusual filler usage, are common among children with Autism Spectrum Disorder (ASD). This study investigated "um" and "uh" usage in children with ASD and typically developing (TD) controls. We analyzed transcribed Autism Diagnostic Observation Schedule (ADOS) sessions for 182 children (117 ASD, 65 TD), aged 4 to 15. Although the groups did not differ in "uh" usage, the ASD group used fewer "ums" than the TD group. This held true after controlling for age, sex, and IQ. Within ASD, social affect and pragmatic language scores did not predict filler usage; however, structural language scores predicted "um" usage. Lower "um" rates among children with ASD may reflect problems with planning or production rather than pragmatic language. En ligne : https://doi.org/10.1007/s10803-022-05565-4 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=508

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