Acoustic feature extraction using ERB like wavelet sub-band perceptual Wiener filtering for noisy speech recognition

In the recent years Wavelet packet (WP) transform has been used as an important speech representation tool. WP based acoustic features have found to be more effective than short time Fourier transform (STFT) based features to capture the information of unvoiced phoneme in continuous speech. In this paper, a new 24 sub-band Equivalent Rectangular Bandwidth (ERB) like wavelet filter is proposed by employing perceptual wiener filter on each sub-band of decomposed noisy speech. Wiener filtered output is then proceed according to the Johnston model to calculate Auditory masking threshold for each wavelet decomposed sub-band. This threshold is used to design the perceptual sub-band weighting (PSW) filter. The output from each perceptually weighted sub-band is processed further to calculate acoustic front end features. This technique aims to enhance the noisy speech signal by using standard Wiener filter on psychoacoustically motivated decomposed wavelet sub-band by controlling the sub-band weighting factor. Hindi continuous digit database is used to evaluate the performance of the proposed feature. Obtained results show that proposed feature is effective for noisy speech recognition compared to some recently proposed feature extraction techniques.