数据采集与处理2012，Vol.27Issue(4)：404-409,6.
区分性训练在声纹密码中的新应用
Novel Application of Discriminative Training in Vocal Password
摘要
Abstract
Due to data sparsity, discriminative training has not been successfully applied to the system of vocal password up to now. Therefor, a novel vocal password framework based on a specific pre-processing strategy is proposed. The new feature is used to represent the distance measure and the problem caused by data sparsity can be solved to some extent. As a consequence , the vocal password is actually transferred from verification to binary classification and the discriminative training of two class models is successfully accomplished on the minimum classification error criteria. After fusing the discriminative system with Gaussian mixture model-universal background model(GMM-UBM) system, the equal error rate(EER) performance decreases to 4. 48%, relatively 17. 95% and 59. 68% lower than the GMM-UBM and the dynamic time warping (DTW) system respectively on the corpus including 60 speakers. The experiment results show that the new application of discriminative training in the vocal password system is feasible and effective.关键词
声纹密码/说话人确认/区分性训练/GMM-UBMKey words
vocal password/ speaker verification/ discriminative training/ GMM-UBM分类
信息技术与安全科学引用本文复制引用
潘逸倩,胡国平,戴礼荣,刘庆峰..区分性训练在声纹密码中的新应用[J].数据采集与处理,2012,27(4):404-409,6.基金项目
安徽省科技攻关(09120201003)资助项目 （09120201003）
