In this paper we examine robust feature extraction methods for automatic speech recognition (ASR) in noise-distorted environments. Previous research showed that combining the coefficients of multi-resolutional modulation frequency band. We show that this multi-resolutional approach can be achieved using a wavelet transform instead of the Fourier transform. Taking the FFT phase into consideration, we applied the Gabor function, which is a complex function, as mother wavelet. This approach yielded a 1.7% increase in recognition accuracy compared to the FFT-based multi-resolutional approach.