摘要: 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。
关键词:
说话人识别,
保局部核,
相关向量机,
高斯核函数,
类内相似度
Abstract: 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.
Key words:
Speaker Recognition(SR),
locality preserving kernel,
Relevance Vector Machine(RVM),
Gaussian kernel function,
intra-class similarity
中图分类号:
郑泽萍, 王万良, 郑建炜. 基于保局部核RVM的说话人识别方法[J]. 计算机工程, 2011, 37(14): 208-210.
ZHENG Ze-Ping, WANG Mo-Liang, ZHENG Jian-Wei. Speaker Recognition Method Based on RVM Using Locality Preserving Kernel[J]. Computer Engineering, 2011, 37(14): 208-210.