Noise Robust Speaker Identification Using RASTA-MFCC Feature with Quadrilateral Filter Bank Structure

This paper motivates the use of Relative Spectra-Mel Frequency Cepstral Coefficients (RASTA-MFCC) feature extracted from the newly designed Quadrilateral filter bank structure and Gaussian Mixture Model-Universal Background Model (GMM-UBM) for improved text independent speaker identification under noisy environment. Unlike neural network model which requires retraining of entire database when a new sample is added to it, GMM-UBM model does not require retraining of entire database which leads to easier and faster processing. RASTA-MFCC is found to be more robust to noisy environment compared with traditional MFCC method. MFCC is an efficient feature for identifying the speaker as it has speaker specific information capturing ability. RASTA processing of speech improves the performance of recognizer in the presence of convolution and additive noise. This work combines the better of these two processes to yield RASTA-MFCC feature which is robust to noise and also proposes a new Quadrilateral filter bank structure which approximates the response of cochlear membrane of human ear to effectively capture the feature vectors. The proposed Quadrilateral filter bank structure with RASTA-MFCC feature and GMM-UBM modeling for speaker identification demonstrates supremacy over triangular and Gaussian filter banks and offers a speaker identification accuracy of 97.67 % for the MEPCO noisy speech database with 50 speakers.