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In this paper a noise robust speech feature extraction algorithm using wavelet packet decomposition (WPD) of the speech signal is presented. In contrast to the time-frequency signal representation based on short-time Fourier transform (STFT), a computational efficient WPD can lead to good representation of stationary (vowel phonemes) as well as non-stationary (consonants) segments of the speech signal. In the proposed WPD scheme a novel wavelet function is developed and presented. The noise robustness is improved with the application of proposed wavelet based denoising algorithm with the modified soft thresholding procedure. For decorrelation of feature vector elements and dimensionality reduction of final feature vector a principal component analysis (PCA) is used. Automatic speech recognition results on Aurora 3 database show performance improvement when compared to the standardized mel-frequency cepstral coefficients (MFCC) feature extraction algorithm.