Modified PCA Speaker Identification Based System Using Wavelet Transform and Neural Networks
نویسندگان
چکیده
This work investigates to improve the robustness of the speaker identification systems based on a modified version of Principal Component Analysis (PCA) and Continuous Wavelet Transform (CWT). Therefore, this work proposes a robust feature extraction method based on MPCA instead of Mel Frequency Cepstral Coefficient (MFCC) that is used in the literature, which is based on converting the common Eigen matrix from two dimensional into a one dimensional one. A simulation program has been built to proof the given mathematical model for the proposed work. At a certain SNR level of the CWT (6dB) the achieved improvement in the classification process was approximately 7.3% (8592.3%) over the previously published work that was based on the MFCC with CWT.
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