Biometric templates are vulnerable to various attacks because they involve user’s privacy.In this paper,by combining the defined formalized representation of random projection with the mainstream text-independentspeaker recognition,a template protection method based on random projection is proposed for voiceprint-biometricsystems.In the enrollment stage,voiceprint characteristic data are projected onto a random space,a Gaussian mixturemodel ( GMM) is then constructed,and the corresponding model parameters are stored as a template.In theverification stage,the voiceprint characteristics to be verified are matched with the model base data in the same randomspace.Moreover,the performance and security of the proposed method are theoretically analyzed.Experimentalresults show that suitable dimensionality reduction helps to improve the system security and approximately maintainthe performance of GMM,while the existing vector quantization method may result in a performance degradationof more than 8%,which means that the random projection is more suitable for the template protection of GMMvoiceprint-biometric systems.