Speaker Recognition Using Spectral Cross-correlation: A Fast Algorithm

Abstract

This study presents an original algorithm for computing the cross-correlation function applied for speech recognition A spectral correlation estimation algorithm based on the comparing the magnitude spectrum of the two signals is presented. The number of samples is reduced by a factor of two, after eliminating the image spectrum. A moving average filter is used to smooth the magnitude spectrum and a re-sampling is performed in the frequency domain, which reduces the spectrum size, by a factor of 8. The algorithm shows good results in recognizing the voice of a specific person, hence its application in speaker identification.