基于保局部核RVM的说话人识别方法

针对说话人语音特征随音量、情绪、健康等因素变化呈现出的复杂分布结构，提出一种基于保局部核相关向量机(RVM)的说话人识别方法。在RVM模型所采用的高斯核函数中引入相似度因子，以保留数据局部结构，构成保局部核RVM模型。在模型训练过程中采用快速算法以避免大型矩阵逆操作，减少计算量，可适用于大样本场合。应用结果表明，该方法能加快测试速度，提高分类精度。

Speaker Recognition Method Based on RVM Using Locality Preserving Kernel

Taking account of the complex structure of the speech features, which is affected by the change of volume, emotion, health and other factors, a new method for Speaker Recognition（SR） based on Relevance Vector Machine（RVM） using locality preserving kernel is proposed. RVM using locality preserving kernel introduces intra-class similarity into Gaussian kernel function to keep the data set＇s neighborhood structure, and is applied into SR. For the purpose of avoiding the inverse matrix operation and applying to a larger sample, the new method uses a fast algorithm for training. Experimental results show that the new classifier model speeds up the test speed and improves the classification accuracy.