Identifying a person's identity still often uses an ID card (KTP, SIM, passport, etc.). This method has a weakness because the ID Card is easily damaged and lost. Biometric recognition systems provide a solution by using human body parts as identity recognition. Sounds are readily available biometric information. Voice pattern recognition is used for the speaker identification process to obtain the identity of someone speaking. This paper reviews several feature extraction and classification methods that are often used in speaker identification. The selection of feature extraction methods and classification functions in computation and the level of accuracy of the speaker identification system. Based on the survey dataset applied with the feature extraction method, the Mel Frequency Cepstral Coefficients (MFCC) method has high accuracy even with noise input. Then in classification, the Gaussian Mixture Model (GMM) method is most often used because it can work in noise. Recently, a hybrid classifier has been developed, which increases the accuracy value.