20.500.12556/DKUM-25903 A noise robust feature extraction algorithm using joint wavelet packet subband decomposition and AR modeling of speech signals This paper presents a noise robust feature extraction algorithm NRFE using joint wavelet packet decomposition (WPD) and autoregressive (AR) modeling of a speech signal. In opposition to the short time Fourier transform (STFT)-based time-frequency signal representation, wavelet packet decomposition can lead to better representation of non-stationary parts of the speech signal (e.g. consonants). The vowels are well described with an AR model as in LPC analysis. The proposed Root-Log compression scheme is used to perform the computation of the wavelet packet parameters. The separately extracted WPD and AR-based parameters are combined together and then transformed with the usage of linear discriminant analysis (LDA) to finally produce a lower dimensional output feature vector. The noise robustness is improved with the application of proposed wavelet-based denoising algorithm with a modified soft thresholding procedure and time-frequency adaptive threshold. The proposed voice activity detector based on a skewness-to-kurtosis ratio of the LPC residual signal is used to effectively perform a frame-dropping principle. The speech recognition results achieved on Aurora 2 and Aurora 3 databases show overall performance improvement of 44.7% and 48.2% relative to the baseline MFCC front-end, respectively. automatic speech recognition autoregressive modeling modified soft thresholding noise robust speech parameterization wavelet packet decomposition true false false Angleški jezik Angleški jezik Neznano 2012-05-31 11:11:36 2012-05-31 11:11:37 2022-04-15 03:40:20 2023-12-31 03:28:03 2007 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 621.39 11170838 0165-1684 URN:SI:UM:DK:V5ZMGXBK http://dx.doi.org/10.1016/j.sigpro.2006.10.009 1 https://dk.um.si/Dokument.php?lang=slv&id=34790 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0