Open AccessAnalysis of acoustic voice parameters for larynx pathology detection
Author(s) -
Maxim Vashkevich,
А. А. Бурак,
Н. С. Конойко,
В. С. Долдова
Publication year - 2020
Publication title -
informatika
Language(s) - English
Resource type - Journals
eISSN - 2617-6963
pISSN - 1816-0301
DOI - 10.37661/1816-0301-2020-17-1-78-86
Subject(s) - larynx , vocal folds , classifier (uml) , logistic regression , mel frequency cepstrum , speech recognition , pattern recognition (psychology) , computer science , pathology , medicine , artificial intelligence , anatomy , feature extraction , machine learning
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 %).