Vocal acoustic analysis is becoming a useful tool for the classification and recognition of laryngological
pathologies. This technique enables a non-invasive and low-cost assessment of voice disorders, allowing
a more efficient, fast, and objective diagnosis. In this work, ANN and SVM were experimented on
to classify between dysphonic/control and vocal cord paralysis/control. A vector was made up of 4
jitter parameters, 4 shimmer parameters, and a harmonic to noise ratio (HNR), determined from 3
different vowels at 3 different tones, with a total of 81 features. Variable selection and dimension
reduction techniques such as hierarchical clustering, multilinear regression analysis and principal
component analysis (PCA) was applied. The classification between dysphonic and control was made
with an accuracy of 100% for female and male groups with ANN and SVM. For the classification
between vocal cords paralysis and control an accuracy of 78,9% was achieved for female group with
SVM, and 81,8% for the male group with ANN.
