This paper presents a novel noise-robust feature
extraction method for speech recognition using the robust perceptual minimum variance distortionless response (MVDR) spectrum of temporally filtered autocorrelation sequence. The perceptual
MVDR spectrum of the filtered short-time autocorrelation
sequence can reduce the effects of residue of the nonstationary
additive noise which remains after filtering the autocorrelation.
To achieve a more robust front-end, we also modify the robust
distortionless constraint of the MVDR spectral estimation method
via revised weighting of the subband power spectrum values
based on the sub-band signal to noise ratios (SNRs), which adjusts
it to the new proposed approach. This new function allows the
components of the input signal at the frequencies least affected by
noise to pass with larger weights and attenuates more effectively
the noisy and undesired components. This modification results
in reduction of the noise residuals of the estimated spectrum
from the filtered autocorrelation sequence, thereby leading to
a more robust algorithm. Our proposed method, when evaluated
on Aurora 2 task for recognition purposes, outperformed all Mel frequency cepstral coefficients (MFCC) as the baseline, relative autocorrelation sequence MFCC (RAS-MFCC), and the MVDR-based features in several different noisy conditions
