Comparative Analysis of MFCC, LFCC, RASTA-PLP

A human?s voice has various parameters that convey vital information. Speech feature extraction follows preprocessing of the speech signal. This process makes certain that the speech feature extraction contains true and accurate information that reveals the emotions of the speaker. In this paper, we present a study and comparison of feature extraction methods like Mel-Frequency Cepstral Co-efficient (MFCC), Linear Predictive Cepstral Co-efficient (LPCC), and Relative Spectral Analysis Perceptual Linear Prediction (RASTA-PLP). These techniques will be analyzed for their suitability and usage in recognition of the speaker. The experimental results show that the better recognition rate is obtained for MFCC as compared to LPCC and RASTA-PLP.