Classification of voice disorders using i-Vector analysis
Kshipra Naikare, Jagannath H. Nirmal, Nikunj R. Lad · 2018
This paper is based on classification of various voice disorders using non-invasive methods with the help of Machine Learning algorithms. In this work, voice samples for three disorders - Dysphonia, Vocal Fold Paralysis and Laryngitis along with normal speech samples are considered. A comprehensive database for each category (4 classes) is created. Using speech processing and feature extraction techniques, the relevant features are extracted, which are stored in a supervector using GMM-UBM. This supervector is then projected on a low dimensional feature vector known as `i-Vectors' using total variability factor analysis. These extracted features are stored and a feature matrix is created. The parameters obtained hereafter are used to train the system using Support Vector Machines, Naïve Bayes and K-NN. The trained system is capable of classification of voice disorders. The accuracy using the above-mentioned classifiers is in the range of 84% to 96%.