Gender Recognition from Analysis of Speech Signals Using GMM Machine Learning Algorithm

Gender identification of a speaker, which is the everyday distinguishing speech's characteristic. It can effortlessly be identified by an individual who hears it. It is substantially vital for many applications to identify gender information driven from signals of speech. With the help of gender recognition, the systems which are dependent on gender are defined. Proper gender identification can increase the efficiency and robustness of any gender-dependent system. In this research, Identification of gender is developed using MFCC coefficients and other acoustic properties taken from signals of speech with GMM. The experiment is conducted on the dataset (SLR45), a free American English corpus from Surfingtech, which contains the utterances of ten speakers (ten females and ten males). Here, we determine the gender of a speaker using MFCC and other acoustic features, and GMM and five other machine learning algorithms (SVM, gradient boosting, neural network, decision tree, forest random) for gender classification. The results achieved show that GMM and Gradient boosting perform better using MFFCC and other acoustic features.