This paper is mainly focused on showing experimental results of a feature extraction algorithm that combines spectral noise reduction and nonlinear feature normalization. The successfulness of this approach has been shown in a previous work, and in this one, we present several improvements that result in a performance comparable to that of the recently approved AFE for DSR. Noise reduction is now based on a Wiener filter instead of spectral subtraction. The voice activity detection based on the full-band energy has been replaced with a new one using spectral information. Relative improvements of 24.81% and 17.50% over our previous system are obtained for AURORA 2 and 3 respectively. Results for AURORA 2 are not as good as those for the AFE, but for AURORA 3 a relative improvement of 5.27% is obtained.