  The most pressing challenge in the field of voice biometrics is selecting the
most efficient technique of speaker recognition. Every individual's voice is
peculiar, factors like physical differences in vocal organs, accent and
pronunciation contributes to the problem's complexity. In this paper, we
explore the various methods available in each block in the process of speaker
recognition with the objective to identify best of techniques that could be
used to get precise results. We study the results on text independent corpora.
We use MFCC (Melfrequency cepstral coefficient), LPCC (linear predictive
cepstral coefficient) and PLP (perceptual linear prediction) algorithms for
feature extraction, PCA (Principal Component Analysis) and tSNE for
dimensionality reduction and SVM (Support Vector Machine), feed forward,
nearest neighbor and decision tree algorithms for classification block in
speaker recognition system and comparatively analyze each block to determine
the best technique
