Voiced-unvoiced classification of speech using autocorrelation matrix

In this paper, a fast method for voiced-unvoiced classification of speech signals is introduced. The suggested method makes the V-UV decision, using signal energy, the peak-to-peak difference of the autocorrelation function, number of zero crossings of the autocorrelation function and the unit delay autocorrelation coefficient all together. This method has been tested on speeches of three speakers, one woman and two men, which include both the speech waveform and the laryngograph signal in stereo form. Having labeled the speech using the laryngograph signal manually, comparison of the hand-labelled decisions and those of the proposed method is achieved. The accuracy of the proposed method is found to be 100% for woman and 98% for men.