Feature Comparison among Various Wavelets in Speaker Recognition Using Support Vector Machine

In this paper, there are 17 types of wavelet coefficients obtained from the Matlab software and an Aurora-2 database used to evaluate which wavelet type has a better accuracy in speaker recognition. We first determine the frequency cepstral coefficient (FCC) level to form a 114-dimentional feature vector by the use of Daubechies-4 wavelet and support vector machines (SVMs) with pre-selected exponential radial basis kernel function (ERBF) and under some additional conditions. Then, average, for each wavelet, the accuracy of 42 possible combinations about the gender of speakers considered in seven kinds of experiments corresponding to two to eight speakers. The experimental results show that the best accuracy in average will be achieved by using the reverse biorthogonal-3.5 or reverse biorthogonal-3.7 wavelet. The reverse biorthogonal-3.5 wavelet is then chosen to be the proposed wavelet function for speaker recognition in terms of shorter filter length