One of important part on speaker identification is the measurement of sound similarity. This study has compared two of the similarity measurement techniques in the noisy voice. First technique is done by using smallest vector sum of pairs and second technique is done by using frequency of occurrence of smallest vector pairs. Noise in the voice can reduce accuracy of speaker identification significantly. To overcome this problem, the two of similarity measurement was combined with Least Mean Square (LMS) for remove noise. Results of the experiments showed that the use of LMS can improve the accuracy of speaker identification at the two of similarity measurement techniques. Second technique produces better accuracy than first technique. Experimental result also showed improvement of LMS learning rate can improve the accuracy of speaker identification.
A Comparative Study of RBF and MLP Neural Model for Seven Element Dynamic Phased Array Smart Antenna
