A Robust Speaker Identification System Based on Wavelet Transform

摘要

A new approach for extracting significant char- acteristic within speech signal for distinct speaker is presented. Based on the multiresolution property of wavelet transform, quadrature mirror filters (QMFs) derived by Daubechies is used to decompose the input signal into varied frequency channels. Owning to the uncorrelation property of each resolution derived from QMFs. Linear Predict Coding Cepstrum (LPCC) of lower frequency region and entropy information of higher frequency re- gion for each decomposition process are calculated as the speech feature vectors.