In the problems of practical application, GMM - UBM is adopted as the background model when the training data is insufficient in speaker recognition system. EigenVoice is used as adaptation ways, then it structured a new combination kernel function combined with homogeneous polynomial kernel with good generalization ability and radial basis kernel function with good earning ability by linear weighted method to optimize model parameter. The optimal parameters of kernel function are determined through the multiple grid search method. DAG method is adopted to realize multivariate classification of SVM kernel function. Then the linear kernel, homogeneous polynomial kernel, radial basis kernel function and combination kernel function are evaluated in the experiments. The experimental results show that the identify performance of the combination kernel is more ideal than that of other kernel functions in the different classification strategy, different adaptive time, different signal-to-noise ratio and different number of speakers.%针对说话人识别实际应用中训练数据不足的问题，选取 GMM-UBM 作为基准系统模型，用 EigenVoice对其作自适应，应用泛化能力较强的多项式核函数和学习能力较强的径向基核函数进行线性加权组合后的组合核函数进行模型参数优化，并用多重网格搜索法确定核函数的最优参数，采用DAG方法实现SVM核函数的多元分类。在仿真实验中评估了线性核、多项式核、径向基核以及组合核函数，实验结果表明，在采用正确的参数前提下，在不同的多分类策略、自适应时间、信噪比和不同的说话人数量的情况下，组合核函数的识别性能明显都优于其它三个单核函数。
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