【摘要】 在基于支持向量机(SVM)的文本无关的说话人确认中,为提高SVM话者模型的训练效率和区分性能,提出2种基于高斯混合模型(GMM)的冒认话者选取方法——通过GMM概率评分,为每个目标说话人选取最接近的话者作为冒认话者用于SVM话者模型的训练,不仅提高模型的训练效率,而且提高SVM模型的区分性,有效地改进系统性能。在NIST’04 1side-1side数据库上的实验表明该方法的有效性。更多还原

【Abstract】 In text-independent Support Vector Machine(SVM) speaker verification,impostor selection for SVM training directly determines its efficiency and performance.This paper proposes two Gaussian Mixture Model(GMM)-based methods for impostor selection.By GMM likelihoods,the most similar impostors to the target speaker are selected for SVM training,which makes the target speaker models more discriminative.Experiments on text-independent SVM speaker verification in NIST’04 1side-1side data show significant improvement.更多还原