Abstract
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
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