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The AUDIO SIGNALING PROCESS (Speaker Recognition Project ) is to implement a recognizer using Matlab that can identify a person processing their voice. The functions and scripts of Matlab were well documented and parameterized so that they could be used in the future. The basic objective of our project is to recognize and classify the speeches of different people. This classification is mainly based on the extraction of several key features such as Mel Frequency Cepstral Coefficients (MFCC ) from these people's voice signals by using the feature extraction process using MATLAB. The above characteristics may consist of tone, amplitude, frequency, etc. It can be achieved using tools like MATLAB. Using a statistical model as a Gaussian mixture model (GMM ) and features extracted from these speech signals, we constructed a unique identity for each person who enrolled for speaker recognition . Algorithm of estimation and maximization, an elegant and powerful method to find the solution of maximum likelihood for a model with latent variables, to test the subsequent discourses against the database of all the speakers who registered in the database.