N2 - In this paper, a new feature-extraction method is proposed to achieve robustness of speech recognition systems. This method combines the benefits of phase autocorrelation (PAC) with bark wavelet transform. PAC uses the angle to measure correlation instead of the traditional autocorrelation measure, whereas the bark wavelet transform is a special type of wavelet transform that is particularly designed for speech signals. The extracted features from this combined method are called phase autocorrelation bark wavelet transform (PACWT) features. The speech recognition performance of the PACWT features is evaluated and compared to the conventional feature extraction method mel frequency cepstrum coefficients (MFCC) using TI-Digits database under different types of noise and noise levels. This database has been divided into male and female data. The result shows that the word recognition rate using the PACWT features for noisy male data (white noise at 0 dB SNR) is 60%, whereas it is 41.35% for the MFCC features under identical conditions L1 - http://www.journals.pan.pl/Content/101364/PDF/04_paper.pdf L2 - http://www.journals.pan.pl/Content/101364 PY - 2015 IS - No 1 EP - 31 DO - 10.1515/aoa-2015-0004 KW - speech recognition KW - feature extraction KW - phase autocorrelation KW - wavelet transform A1 - Majeed, Sayf A. A1 - Husain, Hafizah A1 - Samad, Salina A. PB - Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics VL - vol. 40 DA - 2015[2015.01.01 AD - 2015.12.31 AD] T1 - Phase Autocorrelation Bark Wavelet Transform (PACWT) Features for Robust Speech Recognition SP - 25 UR - http://www.journals.pan.pl/dlibra/publication/edition/101364 T2 - Archives of Acoustics