Phase Autocorrelation Bark Wavelet Transform (PACWT) Featuresfor Robust Speech Recognition

Journal title

Archives of Acoustics




No 1

Publication authors


speech recognition, feature extraction, phase autocorrelation, wavelet transform

Divisions of PAS

Nauki Techniczne


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.


Committee on Acoustics PAS, PAS Institute of Fundamental Technological Research, Polish Acoustical Society


ISSN 0137-5075 ; eISSN 2300-262X