面向语音识别的SVDD改进算法及仿真研究（英文）  

Improved SVDD for Speech Recognition and Simulation

作　　者：郝瑞[1] 刘晓峰[2] 牛砚波[2] 修磊[1] 

机构地区：[1]山西财经大学信息管理学院,太原030006 [2]太原理工大学,太原030024

出　　处：《系统仿真学报》2017年第5期1014-1020,1027,共8页Journal of System Simulation

基　　金：National Natural Science Foundation of China(61072087);Shanxi University of Finance&Economics School youth fund(QN2015014)

摘　　要：支持向量数据描述(SVDD)将多类样本数据每一类用各自的超球来界定,显著降低了二次规划计算复杂度,更易于解决多类分类问题,因此在语音识别研究领域越来越受到广泛关注,本文针对语音样本分类中特征向量重叠和更新等问题,对现有的SVDD多类分类算法进行了改进,一方面,根据样本所在空间位置,构造超球重叠域决策函数;另一方面,基于类增量学习,实现超球类支持向量的动态改变。仿真实验结果表明,本文所提方法明显缩短了建模时间并且具有更好的识别性能。Support vector data description （SVDD） defines multi-class data by their respective hyper-spheres. The computational complexity of the quadratic programming problem is reduced significantly and it is easier to solve multi-class classification problems. Thus, SVDD has attracted more and more attention in the field of speech recognition research. For the problems of the feature vectors of speech samples overlapping and updating, the conventional SVDD for multi-class classification was improved. On the one hand, the spatial position of the samples was fully used to construct the decision function in overlapping domain of hyper-spheres; On the other hand, based on class incremental learning the dynamic change of support vectors was implemented. Simulation experimental results indicate that the proposed method reduces modeling time obviously and has better recognition performance.

关 键 词：支持向量数据描述 多类分类 决策函数 增量学习 语音识别系统仿真 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]