[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119893-en":3,"doc-seo-119893-105":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},119893,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",7,"Healthcare","AUTOMATIC IDENTIFICATION OF DYSPHONIAS USING MACHINE LEARNING ALGORITHMS - Multiclass voice classification study","Dysphonia is a common symptom linked to respiratory and laryngeal problems, impairing long-term voice quality and requiring objective, accurate diagnostic support. Speech-language pathologists typically use acoustic parameters to distinguish hyperfunctional versus hypofunctional dysphonia, but existing AI approaches often perform only binary “healthy vs. ill” labeling. This work applies harmonic-to-noise ratio, CPP-s, zero crossing rate, and MFCC means (2–19) for multiclass classification across euphony, hyperfunction, and hypofunction using six machine-learning algorithms, evaluated via bootstrap.632 and reported with accuracy confidence intervals (87%–92%).","Submitted: 2023-09-05 | Revised: 2023-10-19 | Accepted: 2023-11-05  \nKeywords: Dysphonia, Machine learning, Multiclass classification, Voice signal  \nMiguel Angel BELLO-RIVERA [0009-0003-6641-3094]* , Carlos Alberto REYES-GARCÍA [0000-0003-4773-9585]** , Tania Cristal TALAVERA-ROJAS [0000-0001-7656-3115]*** ,  \nPerfecto Malaquías QUINTERO-FLORES [0000-0001-7651-4364]****, Rodolfo Eleazar PÉREZ-LOAIZA [0000-0002-6500-258X]*****  \nAUTOMATIC IDENTIFICATION OF DYSPHONIAS USING MACHINE LEARNING ALGORITHMS  \nAbstract  \nDysphonia is a prevalent symptom of some respiratory diseases that affects voice quality, even for prolonged periods. For its diagnosis, speech-language pathologists make use of different acoustic parameters to perform objective evaluations on patients and determine the type of dysphonia that affects them, such as hyperfunctional and hypofunctional dysphonia, which is important because each type requires a different treatment. In the field of artificial intelligence this problem has been addressed through the use of acoustic parameters that are used as input data to train machine learning and deep learning models. However, its purpose is usually to identify whether a patient is ill or not, making binary classifications between healthy voices and voices with dysphonia, but not between dysphonias. In this paper, harmonic-to-noise ratio, cepstral peak prominence-smoothed, zero crossing rate and the means of the Mel frequency cepstral coefficients (2-19) are used to make multiclass classification of voices with euphony, hyperfunction and hypofunction by means of six machine learning algorithms, which are: Random Forest, K nearest neighbors, Logistic regression, Decision trees, Support vector machines and Naive Bayes. In order to evaluate which of them presentsa better performance to identify the three voice classes, bootstrap.632 was used. It is concluded that the best confidence interval ranges from 87% to 92%, in terms of accuracy for the K Nearest Neighbors model. Results can be implemented in the development of a complementary application for the clinical diagnosis or monitoring of a patient under the supervision of a specialist.  \n* Tecnológico Nacional de México, Campus Apizaco, Departamento de Sistemas Computacionales, México, [podriaservirte@gmail.com](podriaservirte@gmail.com)  \n** Instituto Nacional de Astrofísica, Óptica y Electrónica, Departamento de Ciencias y Tecnologías Biomédicas, México, [kargaxxi@inaoep.mx](kargaxxi@inaoep.mx)  \n*** Universidad Autónoma de Asunción, Facultad de Ciencias de la Salud, Departamento de Neuropsicología, Paraguay, [ttalavera@uaa.edu.py](ttalavera@uaa.edu.py)  \n**** Tecnológico Nacional de México, Campus Apizaco, Departamento de Sistemas Computacionales, México, [perfecto.qf@apizaco.tecnm.mx](perfecto.qf@apizaco.tecnm.mx)  \n***** Tecnológico Nacional de México, Campus Apizaco, Departamento de Sistemas Computacionales, México, [rodolfo.pl@apizaco.tecnm.mx](rodolfo.pl@apizaco.tecnm.mx)  \n1. INTRODUCTION  \nDysphonia is a voice disorder characterized by the abnormal loss of typical voice quality due to functional or organic disturbances in the larynx. It can be classified into three primary categories: Functional, Organic, and Mixed (Behlau & Pontes, 1989) .  \nFunctional dysphonia, often associated with emotional stress, poor vocal habits, or excessive voice use in daily activities, is typically accompanied by symptoms such as intermittent hoarseness, vocal projection difficulties, and occasional throat discomfort or tension due to vocal fatigue.  \nConversely, organic dysphonia results from physical laryngeal disorders, including injuries, infections, tumors, or other medical conditions that directly impact the laryngeal structure. It tends to present as persistent hoarseness, significant alterations in vocal quality, and occasional throat pain or discomfort.  \nMixed dysphonias represent a combination of both organic and functional factors, resulting in diverse symptoms, including ","cbCaiabSZVrmqjjp","https://ap.wps.com/l/cbCaiabSZVrmqjjp","pdf",323551,1,12,"English","en",105,"# Introduction\n## Dysphonia classification and clinical relevance\n## Hyperfunctional vs hypofunctional dysphonia\n## Etiology and COVID-19 related voice alterations","[{\"question\":\"How does dysphonia classification relate to treatment decisions?\",\"answer\":\"Dysphonia can be categorized into functional, organic, and mixed types, and also into hyperfunctional and hypofunctional patterns. Each category requires distinct therapeutic approaches, making accurate identification clinically important.\"},{\"question\":\"Why do prior AI methods often limit themselves to binary classification?\",\"answer\":\"Many existing AI studies focus on determining whether a patient is ill or healthy, using acoustic features as inputs for machine learning. This approach usually separates only healthy voices from dysphonic voices rather than distinguishing different dysphonia types.\"},{\"question\":\"Which acoustic features and algorithms are used for multiclass identification in this study?\",\"answer\":\"The approach uses harmonic-to-noise ratio, CPP-s, zero crossing rate, and MFCC mean values (2–19). Six algorithms are evaluated: Random Forest, K nearest neighbors, Logistic regression, Decision trees, Support vector machines, and Naive Bayes.\"}]","AUTOMATIC IDENTIFICATION OF DYSPHONIAS USING MACHINE LEARNING ALGORITHMS - Multiclass voice classification study | PDF",1785726864,30,{"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},"automatic-identification-of-dysphonias-using-machine-learning-algorithms-multiclass-voice-classification-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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/automatic-identification-of-dysphonias-using-machine-learning-algorithms-multiclass-voice-classification-study/119893/",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},"How does dysphonia classification relate to treatment decisions?","Question",{"text":75,"@type":76},"Dysphonia can be categorized into functional, organic, and mixed types, and also into hyperfunctional and hypofunctional patterns. Each category requires distinct therapeutic approaches, making accurate identification clinically important.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why do prior AI methods often limit themselves to binary classification?",{"text":80,"@type":76},"Many existing AI studies focus on determining whether a patient is ill or healthy, using acoustic features as inputs for machine learning. This approach usually separates only healthy voices from dysphonic voices rather than distinguishing different dysphonia types.",{"name":82,"@type":73,"acceptedAnswer":83},"Which acoustic features and algorithms are used for multiclass identification in this study?",{"text":84,"@type":76},"The approach uses harmonic-to-noise ratio, CPP-s, zero crossing rate, and MFCC mean values (2–19). Six algorithms are evaluated: Random Forest, K nearest neighbors, Logistic regression, Decision trees, Support vector machines, and Naive Bayes.","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},[93,97,101,105,110,115,118,122,127,130,134],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":29,"slug":121},8,"Research & Report","research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]