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Publication . Article . 2020

Analysis of acoustic voice parameters for larynx pathology detection

M. I. Vashkevich; A. A. Burak; N. S. Kanoika; V. S. Daldova;
Open Access   Russian  
Published: 01 Mar 2020 Journal: Informatika, volume 17, issue 1, pages 78-86 (issn: 1816-0301, Copyright policy )
Publisher: The United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Abstract
The comparative study of two types of voice signal representation for larynx pathology detection is presented. Parameters obtained in clinical system lingWaves compared to parameters obtained by mel-frequency cepstral analysis. The classifier based on the probabilistic model (logistic regression) was designed to determine the suitability of given parameters for the larynx pathology detection problem. To train the classifier, the base of voice samples of 60 persons was recorded, 30 of which constitute the control group, and the other 30 had various diseases of the larynx (nodules of the vocal folds, laryngeal paralysis, or functional dysphonia). The results show that the classifier based on mel-frequency cepstral parameters (83,8 %) higher than the classifier based on parameters obtained in lingWaves (60,4 %).
Subjects by Vocabulary

Medical Subject Headings: otorhinolaryngologic diseases

Microsoft Academic Graph classification: Laryngeal paralysis medicine.disease medicine Logistic regression Classifier (UML) Cepstrum Vocal folds medicine.anatomical_structure Pattern recognition Cepstral analysis Voice analysis Larynx Artificial intelligence business.industry business Mathematics

Subjects

voice analysis, acoustic voice parameters, cepstral analysis, voice pathology detection, logistic regression, Electronic computers. Computer science, QA75.5-76.95

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