A Comparative Study of Machine Learning Models for Gender Recognition from Voice Samples

Abstract: Voice recognition for gender has come a prominent area of study in machine literacy and speech processing. Dimorphism, or the clear physiological and aural distinctions between man and woman voices, is a point of mortal voices that allows automated systems to determine gender grounded on oral traits like pitch, frequency, accentuation, and speech rate. This study investigates how aural features taken from recorded speech can be used to classify gender using machine literacy algorithms. The delicacy and effectiveness of several bracket algorithms are compared through perpetration and evaluation. According to the analysis, woman voices have slightly advanced frequentness than man voices. Mean frequency of man and woman voice is thick between 0.15- 0.20.

Data mining Classifiers, logistic regression, Decision tree, SVM, ANN, Naive Bayesian classifier, Python

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