Gender Voice Recognition Using Matlab

Abstract

Gender identification from speech signals is a key aspect of modern speech processing and human–computer interaction systems. Accurate detection of a speaker’s gender can improve system efficiency by enabling optimized processing and personalized responses. Traditional methods for gender classification primarily rely on extracting acoustic features followed by machine learning-based classification.This paper presents a gender voice recognition approach that utilizes both statistical and spectral features derived from speech signals. Important features such as mean, zero-crossing rate, standard deviation, amplitude, and a set of significant feature vectors are extracted from each audio sample. These features are then used to train and evaluate multiple classification models, including Random Forest, K-Nearest Neighbors (KNN), Logistic Regression, Decision Tree, and Convolutional Neural Networks (CNNs).A comparative analysis of these models is conducted using performance metrics such as accuracy and precision. Experimental results indicate that the CNN-based model outperforms traditional classifiers, achieving superior precision and overall classification performance. The system is implemented using MATLAB, leveraging its capabilities in signal processing and machine learning.The proposed approach demonstrates an effective and scalable solution for automatic gender classification from speech, with potential applications in voice assistants, biometric systems, and intelligent communication platforms.