  An efficient, and intuitive algorithm is presented for the identification of
speakers from a long dataset (like YouTube long discussion, Cocktail party
recorded audio or video).The goal of automatic speaker identification is to
identify the number of different speakers and prepare a model for that speaker
by extraction, characterization and speaker-specific information contained in
the speech signal. It has many diverse application specially in the field of
Surveillance, Immigrations at Airport, cyber security, transcription in
multi-source of similar sound source, where it is difficult to assign
transcription arbitrary. The most commonly speech parametrization used in
speaker verification, K-mean, cepstral analysis, is detailed. Gaussian mixture
modeling, which is the speaker modeling technique is then explained. Gaussian
mixture models (GMM), perhaps the most robust machine learning algorithm has
been introduced examine and judge carefully speaker identification in text
independent. The application or employment of Gaussian mixture models for
monitoring & Analysing speaker identity is encouraged by the familiarity,
awareness, or understanding gained through experience that Gaussian spectrum
depict the characteristics of speaker's spectral conformational pattern and
remarkable ability of GMM to construct capricious densities after that we
illustrate 'Expectation maximization' an iterative algorithm which takes some
arbitrary value in initial estimation and carry on the iterative process until
the convergence of value is observed,so by doing various number of experiments
we are able to obtain 79 ~ 82% of identification rate using Vector quantization
and 85 ~ 92.6% of identification rate using GMM modeling by Expectation
maximization parameter estimation depending on variation of parameter.
