Dimensionality Reduction in Feature Vector using Principle Component Analysis (PCA) for Effective Speaker Recognition

Abstract

ABSTACT This paper describes analysis of a speaker recognition model based on Generalized Gamma Distribution (GGD) using PCA. The proposed work mainly concentrates on the feature vectors that are generated from the speech signals contain high dimension data, but to model a speech and recognize a speaker finite speech samples which plays significant role in speech analysis are sufficient, hence it necessary to reduce dimension of the data. The PCA is considered for this purpose, it converts high dimension speech signal in to a low dimension speech signal by transforming the un-correlated components of the speech signal. PCA not only reduces the correlation among feature vectors but also the speech signal. The feature vectors are modeled by extracting MFCC followed by PCA for dimensionality reduction.