摘要
针对PCA没有有效利用样本的类别信息而导致方言识别率低的问题,采用PCA和LDA组合方法进行特征提取。首先用PCA对普通话、上海话、广东话和闽南话四种方言进行降维,然后在降维后的空间中用LDA进一步特征提取,最后将该特征向量送入BP神经网络进行辨识。仿真实验结果表明,基于PCA和LDA的方言识别的平均识别率高达85%。
In order to solve the low dialect identification rate because PCA doesn＇t effectively use the sample classification information,a method of feature extraction using PCA and LDA is employed.In this paper,PCA is used to effectively reduce the dimensions of Mandarin,Shanghainese,Cantonese,Minnanese,and then LDA is adopted to extract feature vectors from the dimension-reduced space as the input vectors with BP neural network to recognize.The Simulation results demonstrate that the average dialect identification rate based on PCA and LDA can be up to 85%.
出处
《计算机系统应用》
2012年第5期169-171,179,共4页
Computer Systems & Applications
基金
国家自然科学基金(61075008)
