[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123384-en":3,"doc-seo-123384-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},123384,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Speaking with mask in the COVID-19 era - Multiclass machine learning classification of acoustic and perceptual parameters","The intensive use of personal protective equipment often increases voice intensity and can contribute to voice disorders. This paper applies machine learning to analyze how different mask types affect sustained vowels /a/, /i/, and /u/ and the sequence /a’jw/ in a standardized sentence. Objective acoustic parameters and subjective perceptual ratings support statistical comparisons and multivariate classification experiments. Results show significant differences between mask+shield and no-mask, and between mask and mask+shield conditions. Power spectral density decreases significantly above 1.5 kHz when wearing masks, while ratings confirm increasing discomfort from no-mask to protective masks and shield. Machine learning, including random forest models, distinguishes seven mask conditions with up to 94% validation accuracy, separates masked from unmasked with up to 100% validation accuracy, detects shield presence with up to 86% accuracy, and can distinguish male from female in masked conditions with up to 100% validation accuracy. Combining acoustic and perceptual analysis provides a robust way to characterize mask configurations and quantify discomfort.","FEBRUARY 16 2023  \nSpeaking with mask in the COVID-19 era: Multiclass machine learning classification of acoustic and perceptual parameters 􀀈  \nF. Calà; C. Manfredi; L. Battilocchi; ... [et. al](et. al)  \nJAcoust Soc Am 153, 1204–1218 (2023)  \n[https://doi.org/10.1121/10.0017244](https://doi.org/10.1121/10.0017244)  \nSelectable Content List  \nEffect of temperature on the acoustic response and stability of size-isolated protein-shelled ultrasound contrast agents and SonoVue  \nIntra-and inter-speaker variation in eight Russian fricatives  \nAge-related reduction of amplitude modulation frequency selectivity  \nLow frequency ambient noise dynamics and trends in the Indian Ocean, Cape Leeuwin, Australia Estimating cochlear impulse responses using frequency sweeps  \n􀀭  \nView Online  \n􀀱  \nExport Citation  \nCrossMark  \nRelated Content  \nDevelopment of a rapid plasma decontamination system for decontamination and reuse of filtering facepiece respirators  \nAIP Advances (October 2021)  \nApplication of machine learning on brain cancer multiclass classification  \nAIP Conference Proceedings (July 2017)  \nMulticlass sound event detection for respiratory disease diagnosis  \nJAcoust Soc Am (October 2020)  \nDownloaded from [http://pubs.aip.org/asa/jasa/article-pdf/153/2/1204/16653959/1204_1_online.pdf](http://pubs.aip.org/asa/jasa/article-pdf/153/2/1204/16653959/1204_1_online.pdf)  \nARTICLE  \n...................................  \nSpeaking with mask in the COVID-19 era: Multiclass machine learning classification of acoustic and perceptual parameters  \nF. Cal , 1,a) C. Manfredi, 1 L. Battilocchi,2,b) L. Frassineti, 1,c) and G. Cantarella2,b) 1Department of Information Engineering, Universit degli Studi di Firenze, Firenze, Italy 2Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy  \nABSTRACT:  \nThe intensive use of personal protective equipment often requires increasing voice intensity, with possible development of voice disorders. This paper exploits machine learning approaches to investigate the impact of different types of masks on sustained vowels /a/, /i/, and /u/ and the sequence /a’jw/ inside a standardized sentence. Both objective acoustical parameters and subjective ratings were used for statistical analysis, multiple comparisons, and in multivariate machine learning classification experiments. Significant differences were found between maskþshield configuration and no-mask and between mask and maskþshield conditions. Power spectral density decreases with statistical significance above 1.5 kHz when wearing masks. Subjective ratings confirmed increasing discomfort from no-mask condition to protective masks and shield. Machine learning techniques proved that masks alter voice production: in a multiclass experiment, random forest (RF) models were able to distinguish amongst seven masks conditions with up to 94% validation accuracy, separating masked from unmasked conditions with up to 100% validation accuracy and detecting the shield presence with up to 86% validation accuracy. Moreover, an RFclassifier allowed distinguishing male from female subject in masked conditions with 100% validation accuracy. Combining acoustic and perceptual analysis represents a robust approach to characterize masks configurations and quantify the corresponding level of discomfort.  2023 Acoustical Society of America.  \n[https://doi.org/10.1121/10.0017244](https://doi.org/10.1121/10.0017244)  \n(Received 9 August 2022; revised 23 January 2023; accepted 26 January 2023; published online 16 February 2023)  \n[Editor: James F. Lynch] Pages: 1204–1218  \nI. INTRODUCTION  \nThe intensive use of personal protective equipment (PPEs), social distancing, and isolation represent the most important strategies that the World Health Organization (WHO) and local governments put in place in response to the SARS-CoV-2 pandemic outbreak to reduce the spread of the virus. However, as a major negative consequence, communication between individuals was deeply","cbCaiocorqD1L7w5","https://ap.wps.com/l/cbCaiocorqD1L7w5","pdf",2667228,1,16,"English","en",105,"# Abstract\n# Introduction\n## PPE and communication impact\n## Occupational voice users and clinical settings\n# Mask types and design characteristics\n## Surgical masks\n## FFP2 (N95) masks\n## FFP3 masks","[{\"question\":\"What is the document’s main goal regarding mask use and voice?\",\"answer\":\"To investigate how different mask types influence voice production during sustained vowels and a standardized sentence sequence, and to quantify resulting acoustic and perceptual effects.\"},{\"question\":\"Which data types are used for analysis and classification?\",\"answer\":\"Both objective acoustic parameters and subjective ratings are used for statistical analysis and multivariate machine learning classification experiments.\"},{\"question\":\"How effective were the machine learning models in distinguishing mask conditions?\",\"answer\":\"Random forest models distinguished seven mask conditions with up to 94% validation accuracy, separated masked from unmasked with up to 100% validation accuracy, and detected shield presence with up to 86% validation accuracy.\"}]","Speaking with mask in the COVID-19 era - Multiclass machine learning classification of acoustic and perceptual parameters | PDF",1785816218,40,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"speaking-with-mask-in-the-covid-19-era-multiclass-machine-learning-classification-of-acoustic-and-perceptual-parameters","",{"@graph":36,"@context":86},[37,54,69],{"@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/speaking-with-mask-in-the-covid-19-era-multiclass-machine-learning-classification-of-acoustic-and-perceptual-parameters/123384/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the document’s main goal regarding mask use and voice?","Question",{"text":76,"@type":77},"To investigate how different mask types influence voice production during sustained vowels and a standardized sentence sequence, and to quantify resulting acoustic and perceptual effects.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which data types are used for analysis and classification?",{"text":81,"@type":77},"Both objective acoustic parameters and subjective ratings are used for statistical analysis and multivariate machine learning classification experiments.",{"name":83,"@type":74,"acceptedAnswer":84},"How effective were the machine learning models in distinguishing mask conditions?",{"text":85,"@type":77},"Random forest models distinguished seven mask conditions with up to 94% validation accuracy, separated masked from unmasked with up to 100% validation accuracy, and detected shield presence with up to 86% validation accuracy.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":29,"slug":119},7,"Healthcare","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":107,"slug":138},19,"General","general"]