Abstract: A linear combination of several kernels is used to construct multiple kernel space. In multiple kernel space, Support Vector Machine (SVM) classifiers are designed to identify speakers with short utterances. Multiple kernel mapping can solve the problem of single kernel mapping, such as the selection of kernel function and parameters. Besides, multiple kernel mapping can increase discriminative power among different speakers and improve the performance of classifiers. In simulation experiment, Gaussian Mixture Model (GMM) was used to get GMM supervector as speakers' final feature parameters. Experiment results show that under the condition of short utterances and two noisy environments, the performance and robustness of the multiple SVM-GMM speaker recognition algorithm are better than that of SVM-GMM algorithm.

Key words: communication, speaker recognition, short utterances, multiple kernel SVM, Gaussian mixture model supervector