Automatic Optimized Voice Based Gender Identification for Speech Recognition

In Today's world ecommerce and advertisements sectors becomes very crucial also unavoidable in this scenario identifying gender of a person is important to make ourselves safe and secure. This project identifies gender of a person, by using features of their voice and processing these features using various machine learning algorithms. For this purpose four different types of popular classifiers were used and showed their accuracy. Popular classifiers used here is Random forest MLP neural network, CHAID decision tree and XGBOOST using these classifiers the voice can be classified as male or female. Using XGBOOST classifier the accuracy attained is 99.621 which is highest accuracy when compared to other classifiers.