GMM-UBM和SVM在说话人识别中的应用

针对说话识别领域短语音导致的训练数据不充分的问题，选择能够突出说话人个性特征的GMM-UBM作为基线系统模型，并引入SVM解决GMM-UBM导致的系统鲁棒性差的问题. 选择不同的核函数对SVM的识别性能有较大的影响，针对多项式核函数泛化能力较强、学习能力较差与径向基核函数学习能力较强、泛化能力较差的特性，对两种单核核函数进行线性加权组合，以使组合核函数兼具各单核的优点. 仿真实验结果表明，组合核函数SVM的识别率和等错误率明显优于不引入SVM的GMM-UBM的基线系统及其它三个单核函数，并在不同信噪比情况下也兼顾了系统识别准确率与鲁棒性.

Application of GMM-UBM and SVM in Speaker Recognition

Aiming at the problem that training data is insufficient due to little training data in speaker recognition system, this paper adopts GMM-UBM as the background model which can identify the characteristics of the target speaker. And SVM is introduced to solve the problem of poor robustness of the system caused by GMM-UBM. It has much influence on SVM identification performance with different kernel functions. Aiming at the Characteristics of Polynomial kernel with good generalization ability and poor earning ability and Gaussian kernel with good earning ability and poor generalization ability, it structures a new combination kernel function which combines the advantages of each single kernel function by linear weighted method. The experimental results show that the recognition rate and Equal Error Rate of the combination kernel is more ideal than other kernel functions. And it achieves satisfactory recognition rate and robustness in the situations of different signal-to-noise ratio.