Information technology for gender recognition by voice

Abstract

Gender recognition from voice is a challenging problem in speech processing. This task involves extracting meaningful features from speech signals and classifying them into male or female categories. In this article, was implemented a gender recognition system using Python programming. I first recorded voice samples from both male and female speakers and extracted Mel-frequency cepstral coefficients (MFCC) as features. Then trained, a Support VectorMachine (SVM) classifier was on these features and evaluated its performance using accuracy, precision, recall, and F1-score metrics. These experiments demonstrated that proposed system should achieve high accuracy on the test set and will accurately predict the gender of a speaker based on their voice. I also explored using pre-trained models to reduce the need for large amounts of training data and found that they can provide good performance while requiring less computation. This study highlights the potential of using machine learning techniques for gender recognition from voice and can be extended to other speech processing applications.