Implementation of Feature Selection for Optimizing Voice Detection Based on Gender using Random Forest

Abstract

Gender-based voice detection is one of the machine learning applications that has various benefits in technology and services, such as virtual assistants, human-machine interaction systems, and voice data analysis. However, the use of too many features, including irrelevant features, can cause a decrease in accuracy and model performance. This research aims to optimize voice-based gender detection by applying a feature selection method to select significant features based on their correlation value to the target. Experimental results show that by using only the significant features selected through correlation analysis, the accuracy of the model is significantly improved compared to using all available features. This research confirms the importance of feature optimization to support the development of more efficient and accurate gender-based speech detection models.