Abstract
AbstractGender Classification is one of the crucial problems in fields of AI. Can a machine be able to classify Gender (Male/Female)? Yes, the above problem is done using various image classification techniques which uses feature extraction from given set of images. As human being’s we can know the gender of the person when we talk to them, can this be achieved by a machine? This is what we are going to work in this research. This research is going to examine the performances of different Machine Learning algorithms and Deep learning algorithms on Gender Classification based on voice. We have used Multi Layer Perceptron (MLP), Random Forest, Decision Tree and Logistic Regression models and compare their performance to find out the best classifier for our data set. We got 96.84% accuracy using MLP, 96.42% using Random Forest, 96.21% using Decision Tree and 89.37% using Logistic Regression. Multi Layer Perceptron stands high with a modest difference in accuracy.KeywordsMulti layered perceptron (MLP)Random forestDecision treeLogistic regressionGenderVoice
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call