  Speaker Identification process is to identify a particular vocal cord from a
set of existing speakers. In the speaker identification processes, unknown
speaker voice sample targets each of the existing speakers present in the
system and gives a predication. The predication may be more than one existing
known speaker voice and is very close to the unknown speaker voice. The model
is a Gaussian mixture model built by the extracted acoustic feature vectors
from voice. The i-vector based dimension compression mapping function of the
channel depended speaker, and super vector give better predicted scores
according to cosine distance scoring associated with the order pair of
speakers. In the order pair, the first coordinate is the unknown speaker i.e.
test speaker, and the second coordinates is the existing known speaker i.e.
target speaker. This paper presents the enhancement of the prediction based on
i- vector in compare to the normalized set of predicted score. In the
simulation, known speaker voices are collected through different channels and
in different languages. In the testing, the GMM voice models, and GMM based
i-Vector speaker voice models of the known speakers are used among the numbers
of clusters in the test data set.
