摘要
提出一种基于主成分分析(PCA)和多约简支持向量机(SVM)的多级说话人辨识方法。首先用PCA对注册说话人进行快速粗判决,再用多约简SVM进行最后决策。此多约简SVM有两个约简步骤,即用PCA和样本选择算法分别减少训练数据的维数和个数。理论分析和实验结果表明:该方法可以大大减少系统的存储量和计算量,提高训练和识别时间,并具有较好的鲁棒性。
This paper proposed a new hierarchical speaker identification system based on Multi-Reduced Support Vector Machine （MRSVM） and Principal Component Analysis （PCA） classifier to reduce the recognition time of speaker identification. First get a coarse judge by a fast scan of all registered speaker using PCA classifier, and then get a final decision-making by the proposed MRSVM. And the MRSVM has two reduction steps： PCA and kernel-based fuzzy clustering are used to reduce the dimensions and amounts of training data respectively. The experimental results show that the training data, time and storage can be reduced remarkably by using our method, and the system has better robustness.
出处
《计算机应用》
CSCD
北大核心
2008年第1期127-130,共4页
journal of Computer Applications
基金
甘肃省教育厅科研基金资助项目(0603-10)
关键词
多约简支持向量机
模糊核聚类
主成分分析变换
多级说话人辨识
Multi-Reduced Support Vector Machines （MRSVM）
Kernel-based fuzzy clustering
Principal Component Analysis （PCA） transform
Hierarchical Speaker Identification （HSI）
