Autocorrelation and Double Autocorrelation Based Spectral Representations for a Noisy Word Recognition System

摘要

Two methods of spectral analysis for noisy speech recognition are proposed and tested in a speaker independent word recognition experiment under an additive white Gaussian noise environment. One is Mel-frequency cepstral coefficients (MFCC) spectral analysis on the autocorrelation sequence of the speech signal and the other is MFCC spectral analysis on its double autocorrelation sequence. The word recognition experiment shows that both of the proposed methods achieve better results than the conventional MFCC spectral analysis on the input speech signal.