This paper describes a novel application of multiresolution analysis (MRA) in extracting acoustic features that possess de-noising capability for robust speech recognition. The MRA algorithm is used to construct a mel-scaled wavelet packet filter-bank, from which subband powers are computed as the feature parameters for speech recognition. Wiener filtering is applied to a few selected subbands at some intermediate stages of decomposition. For high-frequency bands, Wiener filters are designed based on a reduced fraction of the estimated noise power, making the consonant features such more prominent and contrastive. The proposed method is evaluated in phone recognition experiments with the TIMIT database. In the presence of stationary white noise at 10-dB SNR, the de-noised MRA features attain a phone recognition rate of 32%. There is a noticeable improvement compared with the accuracy of 29% and 20% attained by the commonly used mel-frequency cepstral coefficients (MFCC) with and without cepstral mean normalization (CMN), respectively. The effectiveness of the MRA features is also verified by the fact that they exhibit smaller distortion from clean speech.
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