Study of speech features robustness for speaker verification application in noisy environments

This paper presents a comparative study and evaluation of the performances of four speech feature vectors, i.e., MFCC, IMFCC, LFCC, and PNCC in a speaker verification system based on speaker modeling through the Gaussian mixture model (GMM) under clean and noisy speech conditions. The TIMIT and NOISEX92 dataset were used in implementing the tests for speech signal and noise, respectively. The evaluation results show that IMFCC and PNCC provide superior performance in the presence of noise. In order to enhance the performance of the system under noisy conditions, the application of spectral subtraction algorithm as a pre-processing stage was investigated. It only improved the performance for the speech signal contaminated with white noise.