1. A method for speaker recognition, comprising: obtaining, by using a characteristic parameter of a test voice, a first Gaussian Mixture Model (GMM) and a second GMM, a first score function of the characteristic parameter of the test voice, wherein the first score function is a difference between a logarithm of a first likelihood probability of the characteristic parameter of the test voice on the first GMM and a logarithm of a second likelihood probability of the characteristic parameter of the test voice on the second GMM; obtaining, by using the characteristic parameter of the test voice and a third GMM, a third likelihood probability of the characteristic parameter of the test voice on the third GMM, wherein the third GMM is composed of the first GMM and the second GMM; obtaining a second score function of the characteristic parameter of the test voice according to the first score function and the third likelihood probability, wherein the second score function is equal to an inner product of a first vector and a second vector, the first vector is a vector whose elements are all one, and the second vector is a vector of a difference between an average posterior probability of the characteristic parameter of the test voice on the first GMM and an average posterior probability of the characteristic parameter of the test voice on the second GMM; and re-estimating, by using the second vector of a training recognition trial and through a support vector machine (SVM), the first vector to obtain a third vector and obtain a third score function of the characteristic parameter of the test voice to take the characteristic parameter of the test voice as an input to obtain a corresponding speaker score, wherein the third score function is equal to an inner product of the third vector and the second vector.