基于免疫聚类的RBF网络在说话人识别中的应用

Application of immune algorithm based RBF network to human speaker recognition

针对传统的基于RBF(Radial Bais Function)网络的说话人识别系统中聚类中心的数量和位置难以确定的问题,提出了一种基于人工免疫机制的RBF网络作为分类器的说话人识别系统。采用人工免疫机制可根据输入语音数据集合自适应地确定RBF网络隐层中心的数量和初始位置。实际测试表明,该系统具有快速学习网络权重的能力,并且网络的全局寻优能力强,识别率高,是说话人识别的一种有效可行的新方法。

When using traditional clustering algorithms based on RBF to recognize human speakers, it is hard to decide the number and locations of the cluster centers.To overcome these shortcomings, an RBF network based on artificial immune mechanism for human speaker recognition is proposed.The artificial immune mechanism can adaptively compute the number and initial locations of the centers in the hidden layer of the RBF network based on the audio sample data set.Experimental tests show that the system has a fast learning speed for network weights.The system is very good at searching for global optimum.It has a high recognition rate and is a new practical method for human speaker recognition.