Comparison of Several Methods to Detect Impaired Voices

摘要

Most of vocal and voice diseases cause changes in the voice. These diseases have to be diagnosed and treated during an early stage. There is an increased risk for vocal and voice diseases due to the modern way of life. Acoustic voice analysis is an effective and non-invasive tool due to: a) Objective support of the diagnostics. b) Screening the vocal and voice diseases and especially their early detection. c) Objective determination of the impairment of the vocal function. d) Objective evaluation of the effect of the air pollution on the voice. e) Evaluation of surgical and pharmacological treatments. f) Evaluation of the rehabilitation. Many algorithms to calculate acoustic parameters have been developed and it is demonstrated that there is a great correlation between deviations of parameters and pathologies. The effectiveness and importance of the acoustic analysis of pathological voices has been proven by many experimental researches demonstrating that acoustic parameters of pathologic voices are deviated from the mean. The aim of this paper is to study and compare different methods to be used for the detection of impaired voices. Voice registers are parameterised by means of acoustic parameters, and five different techniques have been applied in order to classify: Minimum Distance, Nearest Neighbour, K-Nearest Neighbour, Fisher analysis, Neural Nets.