Information technology for gender recognition by voice

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