Speaker recognition (SR) or identification is the subset of broad area of Pattern recognition. Given the features of the voice print, the recognition system identifies the speaker from the knowledge of the speaker models stored in the database. In today’s world when many of our works are done through voice, recognition of the speaker is necessary.Recently, SR has also gained importance in Internet of Things (IoT) like setting up of smart environments for home, industries or educational and commercial applications. The race for high accuracy needs making the devices used in these smart environments as close to human hearing capacity as possible. Speaker identification is mostly used to establish negative recognition. Negative recognition is when the
system decides whether a person is who he disagrees to be thus preventing a person from exploiting multiple identities. Only biometrics will be suitable to establish such identification. The feature extraction of voice sample along with comparative analysis of its methods is of fundamental interest in this paper. We try to compare the performance of features which are used in state of art speaker recognition models and analyse variants of Mel frequency cepstrum coefficients (MFCC) predominantly used in feature extraction which can be further incorporated and used in various smart devices.