Pengenalan Jenis Kelamin Manusia Berbasis Suara Menggunakan MFCC dan GMM - Faisal Dharma Adhinata; Diovianto Putra Rakhmadani; Alon Jala Tirta Segara (Indonesian Journal of Data Science, IoT, Machine Learning and Informatics, 2021)
Biometric information that exists in humans is unique from one human to another. One of the biometric data that is easily obtained is the human voice. The human voice is identic data that can differentiate between individuals. When we hear human voices directly, it is easy for our ears to tell the person who is speaking is male or female. But sometimes male voices can resemble girls and vice versa. Therefore, we propose a human voice detection system through Artificial Intelligence (AI) in machine learning. In this study, we used the Mel Frequency Cepstrum Coefficients (MFCC) method to extract human voice features and Gaussian Mixture Models (GMM) for the classification of female or male voice data. The experiment results showed that the system built was able to detect human gender through biometric voice data with an accuracy of 81.18%.