A Survey on Optimization Techniques in Voice Disorder Classification

Abstract

Voice diseases are increasing dramatically, by unhealthy social habits and voice abuse. In this work, we investigated the optimization techniques for voice pathology and voice disorder classification system. The literature review presents a survey of the acoustic analysis for human voice disorder classification using optimization and machine learning techniques with importance to pathology detection and its classification. The voices input signal is given to numerous classifications methods, in such the simplest way the classification produces the result against the pathology voices and normal voices with relevence to the male and female human voice classification. The foremost goal of this analysis work is to provide a entire study of the most popular machine learning techniques namely, Noise Removal and Silence Removal  and different filters in preprocessing, The feature extraction techniques namely, Acoustic features (signal energy, pitch, formant, jitter, shimmer), Reflection coefficients, Autocorrelation, Linear Predictive Coding (LPC), Mel-frequency cepstrum co-efficient (MFCC), Zero crossing with peak amplitude (ZCPA), Dynamic time wrapping (DTW) and Relative spectral processing (RASTA). And to classify the input voice signal with the help of classification algorithms like, Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN).