[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120794-en":3,"doc-seo-120794-105":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},120794,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning-and Statistical-based Voice Analysis of Parkinson's Disease Patients - A Survey","Parkinson’s disease requires timely preliminary diagnosis and assessment of disease presence and severity to better control progression. This review evaluates real-time, non-invasive approaches that use machine learning enhanced voice analysis as a promising direction. Acoustic features are surveyed across widely used and state-of-the-art feature-based machine learning methods, including baselines, public datasets, toolboxes, and metadata. From 102 works plus 5 review articles, the most adopted effective features include Jitter, Shimmer, HNR, F0, and MFCC, with notable prevalence of glottal-like models and filtering options such as DFA.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nMachine learning-and statistical-based voice analysis of Parkinson's disease patients: A survey  \nOriginal  \nMachine learning-and statistical-based voice analysis of Parkinson's disease patients: A survey / Amato, F. ; Saggio, G. ; Cesarini, V. ; Olmo, G. ; Costantini, G.. -In: EXPERT SYSTEMS WITH APPLICATIONS. -ISSN 0957-4174. -219:(2023), p. 119651. [10 . 1016/j.eswa.2023. 119651]  \nAvailability:  \nThis version is available at: 11583/2976268 since: 2023-03-02T10:16:59Z  \nPublisher: Elsevier  \nPublished  \nDOI:10.1016/j.eswa.2023.119651  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nElsevier postprint/Author's Accepted Manuscript  \n© 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license  \n[http://creativecommons.org/l](http://creativecommons.org/l)icenses/by-nc-nd/4 .0/ .The final authenticated version is available online at: [http://dx.doi.org/10.1016/j.eswa.2023.119651](http://dx.doi.org/10.1016/j.eswa.2023.119651)  \n(Article begins on next page)  \nRevised manuscript (Clean Version) Click here to view linked References   \n… et al. / Expert Systems with Applications  \nMachine Learning-and Statistical-based Voice Analysis of Parkinson’s Disease Patients: A Survey  \nFederica Amato 1 , Giovanni Saggio2 , Valerio Cesarini *3 , Gabriella Olmo4 , Giovanni  \nCostantini5  \n1E-mail: federica.amato@polito.it-dept. Control and Computer Engineering, Polytechnic University of Turin, Turin, Italy 2E-mail: [saggio@uniroma2.it-](saggio@uniroma2.it-dept. Electronic Engineering)[dept. Electronic Engineering](saggio@uniroma2.it-dept. Electronic Engineering), University of Rome Tor Vergata, Rome, Italy [3](3E-mail: valerio.cesarini@uniroma2.it-dept. Electronic Engineering)[E-mail:](3E-mail: valerio.cesarini@uniroma2.it-dept. Electronic Engineering)[ valerio.cesarini@uniroma2.it-](3E-mail: valerio.cesarini@uniroma2.it-dept. Electronic Engineering)[dept. Electronic Engineering](3E-mail: valerio.cesarini@uniroma2.it-dept. Electronic Engineering), University of Rome Tor Vergata, Rome, Italy 4E-mail: gabriella.olmo@polito.it-dept. Control and Computer Engineering, Polytechnic University of Turin, Turin, Italy 5E-mail: [costantini@uniroma2.it-](costantini@uniroma2.it-dept. Electronic Engineering)[dept. Electronic Engineering](costantini@uniroma2.it-dept. Electronic Engineering), University of Rome Tor Vergata, Rome, Italy  \n* Corresponding author. Tel.: +39 3336116177  \n[E-mail address:](E-mail address: valerio.cesarini@uniroma2.it)[ valerio.cesarini@uniroma2.it](E-mail address: valerio.cesarini@uniroma2.it)  \n[Address:](Address: Via del Politecnico 1)[ Via del Politecnico 1](Address: Via del Politecnico 1), 00133 Rome, Italy  \n1077-3142/© 2017 Elsevier Inc. All rights reserved.  \n… et al. / Expert Systems with Applications  \nAbstract  \nThe preliminary diagnosis and evaluation of the presence and/or severity of Parkinson’s disease is crucial in controlling the progress of the disease. Real-time, non-invasive methodologies based on machine learning-enhanced voice analysis are gathering more interest as the potential of this field unveils. Specifically, acoustic features are employed in many machine learning techniques, and could also function as indicators of the overall state of the subjects’ voice: this review aims at identifying the most widely employed and promising feature-based machine learning methodologies, evidencing baselines and state-of-the-art solutions. A total of 102 works plus 5 review articles were selected from the IEEE Xplore, PubMed, Elsevier, and Web of Science electronic databases. A statistical assessment is performed identifying the most frequently used features as well as those deemed as most effective; an overview of algorithms, public datasets, toolboxes, and general metadata is also performed. According to our results,","cbCaiu4nCtPmyMJh","https://ap.wps.com/l/cbCaiu4nCtPmyMJh","pdf",1167691,1,38,"English","en",105,"# Abstract\n# Keywords and Abbreviations\n# 1. Introduction\n## Machine learning for diagnosis and monitoring","[{\"question\":\"What is the main goal of this survey?\",\"answer\":\"To identify widely used and promising feature-based machine learning methodologies for analyzing voice in Parkinson’s disease, including baselines and state-of-the-art solutions.\"},{\"question\":\"How many studies and review articles were selected?\",\"answer\":\"A total of 102 works plus 5 review articles were selected from IEEE Xplore, PubMed, Elsevier, and Web of Science.\"},{\"question\":\"Which voice features are most frequently adopted and effective?\",\"answer\":\"Jitter, Shimmer, Harmonic-to-Noise Ratio, Fundamental Frequency, and Mel Frequency Cepstral Coefficients are reported as the mostly adopted features, with additional use of glottal-like models and filtering options such as Detrended Fluctuation Analysis.\"}]","Machine Learning-and Statistical-based Voice Analysis of Parkinson's Disease Patients - A Survey | PDF",1785732067,96,{"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},"machine-learning-and-statistical-based-voice-analysis-of-parkinsons-disease-patients-a-survey","",{"@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/machine-learning-and-statistical-based-voice-analysis-of-parkinsons-disease-patients-a-survey/120794/",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","2026-08-03",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 is the main goal of this survey?","Question",{"text":75,"@type":76},"To identify widely used and promising feature-based machine learning methodologies for analyzing voice in Parkinson’s disease, including baselines and state-of-the-art solutions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How many studies and review articles were selected?",{"text":80,"@type":76},"A total of 102 works plus 5 review articles were selected from IEEE Xplore, PubMed, Elsevier, and Web of Science.",{"name":82,"@type":73,"acceptedAnswer":83},"Which voice features are most frequently adopted and effective?",{"text":84,"@type":76},"Jitter, Shimmer, Harmonic-to-Noise Ratio, Fundamental Frequency, and Mel Frequency Cepstral Coefficients are reported as the mostly adopted features, with additional use of glottal-like models and filtering options such as Detrended Fluctuation Analysis.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]