[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117819-en":3,"doc-seo-117819-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117819,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Acoustic analysis in stuttering: a machine-learning study","Stuttering is a childhood-onset neurodevelopmental disorder impairing speech fluency, currently evaluated through perceptual judgments and clinical scales. This study objectively and automatically characterizes voice in people who stutter using an artificial intelligence approach, specifically a support vector machine (SVM) classifier. Smartphone recordings of vowels and sentences were analyzed for overall discrimination between stutterers and controls, and for age-related effects. Results show high diagnostic accuracy, with likelihood ratios correlating with clinical measures and supporting the biological plausibility of the findings.","TYPE Original Research PUBLISHED 30 June 2023  \nDOI 10.3389/fneur.2023.1169707  \nOPEN ACCESS  \nEDITED BY  \nKaterina Markopoulou,  \nNorthShore University HealthSystem, United States  \nREVIEWED BY  \nCarlo Alberto Artusi, University of Turin, Italy Mammarella Fulvio,  \nSan Camillo-Forlanini Hospital, Italy  \n*CORRESPONDENCE  \nGiovanni Costantini  \n [costantini@uniroma2.it](costantini@uniroma2.it)  \n†These authors have contributed equally to this work  \nRECEIVED 21 February 2023  \nACCEPTED 16 June 2023  \nPUBLISHED 30 June 2023  \nCITATION  \nAsci F, Marsili L, Suppa A, Saggio G, Michetti E, Di Leo P, Patera M, Longo L, Ruoppolo G, Del Gado F, Tomaiuoli D and Costantini G (2023) Acoustic analysis in stuttering: a machinelearning study.  \nFront. Neurol. 14:1169707.  \ndoi: 10.3389/fneur.2023.1169707  \nCOPYRIGHT  \n© 2023 Asci, Marsili, Suppa, Saggio, Michetti, Di Leo, Patera, Longo, Ruoppolo, Del Gado, Tomaiuoli and Costantini. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nAcoustic analysis in stuttering: a machine-learning study  \nFrancesco Asci 1, 2†, Luca Marsili3†, Antonio Suppa 1, 2†,  \nGiovanni Saggio4, Elena Michetti 5, Pietro Di Leo4, Martina Patera 1, Lucia Longo 6, Giovanni Ruoppolo7, Francesca Del Gado 5, Donatella Tomaiuoli 5 and Giovanni Costantini4*  \n1 Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy, 2 IRCCS Neuromed Institute, Pozzilli, Italy, 3 Department of Neurology, James J. and Joan A. Gardner Center for Parkinson’s Disease and Movement Disorders, University of Cincinnati, Cincinnati, OH, United States, 4 Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy, 5CRC – Centro Ricerca e Cura, Rome, Italy, 6 Department of Sense Organs, Otorhinolaryngology Section, Sapienza University of Rome, Rome, Italy, 7 IRCCS San Raffaele Pisana, Rome, Italy  \nBackground: Stuttering is a childhood-onset neurodevelopmental disorder affecting speech fluency. The diagnosis and clinical management of stuttering is currently based on perceptual examination and clinical scales. Standardized techniques for acoustic analysis have prompted promising results for the objective assessment of dysfluency in people with stuttering (PWS) .  \nObjective: We assessed objectively and automatically voice in stuttering, through artificial intelligence (i.e., the support vector machine – SVM classifier) . We also investigated the age-related changes affecting voice in stutterers, and verified the relevance of specific speech tasks for the objective and automatic assessment of stuttering.  \nMethods: Fifty-three PWS (20 children, 33 younger adults) and 71 age−/gendermatched controls (31 children, 40 younger adults) were recruited. Clinical data were assessed through clinical scales. The voluntary and sustained emission of a vowel and two sentences were recorded through smartphones. Audio samples were analyzed using a dedicated machine-learning algorithm, the SVM to compare PWS and controls, both children and younger adults. The receiver operating characteristic (ROC) curves were calculated for a description of the accuracy, for all comparisons. The likelihood ratio (LR), was calculated for each PWS during all speech tasks, for clinical-instrumental correlations, by using an artificial neural network (ANN) .  \nResults: Acoustic analysis based on machine-learning algorithm objectively and automatically discriminated between the overall cohort of PWS and controls with high accuracy (88%) . Also, physiologic ageing crucially influenced stuttering as demonstrated by the high accuracy (92%) of machine-learning a","cbCaie70uMqvsMSZ","https://ap.wps.com/l/cbCaie70uMqvsMSZ","pdf",1747451,1,11,"English","en",105,"# Introduction\n# Background and objective\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses the objective and automatic assessment of stuttering, which is traditionally evaluated using perceptual exams and clinical scales.\"},{\"question\":\"How was the machine-learning model built and tested?\",\"answer\":\"Audio samples were recorded via smartphones and analyzed with a dedicated machine-learning algorithm using an SVM to compare people who stutter with age- and gender-matched controls.\"},{\"question\":\"What were the main findings and diagnostic performance?\",\"answer\":\"Machine-learning acoustic analysis discriminated stutterers from controls with high accuracy, and classification accuracy was also high when separating children and younger adults. Likelihood ratios showed significant correlations with clinical scales.\"}]","Acoustic analysis in stuttering: a machine-learning study | PDF",1785679757,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"acoustic-analysis-in-stuttering-a-machine-learning-study","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/acoustic-analysis-in-stuttering-a-machine-learning-study/117819/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address?","Question",{"text":75,"@type":76},"The study addresses the objective and automatic assessment of stuttering, which is traditionally evaluated using perceptual exams and clinical scales.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the machine-learning model built and tested?",{"text":80,"@type":76},"Audio samples were recorded via smartphones and analyzed with a dedicated machine-learning algorithm using an SVM to compare people who stutter with age- and gender-matched controls.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the main findings and diagnostic performance?",{"text":84,"@type":76},"Machine-learning acoustic analysis discriminated stutterers from controls with high accuracy, and classification accuracy was also high when separating children and younger adults. 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