一种声纹美尔频率倒谱系数干扰消除算法研究  

Interference Elimination Algorithms of Voiceprint Meyer Frequency Cepstrum Coefficient

作　　者：蒋琳琼[1] 贺建飚[2,1] 

机构地区：[1]梧州学院计算机科学系,广西梧州543000 [2]中南大学信息科学与工程学院,湖南长沙410083

出　　处：《计算机仿真》2013年第4期382-385,共4页Computer Simulation

基　　金：2010国家自然科学基金面上项目(51074097);2011广西自然科学基金项目(2011GXNSFA018165);2011广西教育厅科研项目(201102ZD034,201106lx559);2010广西高等学校优秀人才资助计划项目(16-3);梧州学院2010院级科研项目(2010C006);2012广西重大科技攻关项目(桂科攻12118017-6)

摘　　要：在伴随着外部噪声的情况下,待识别的声纹美尔频率倒谱系数特征各项属性很容易受到外部噪声的干扰发生改变,造成声纹特征的识别的精度不高。为提高精度,提出了一种用支持向量机的美尔频率倒谱系数特征干扰去除算法。确定分类决策函数时充分考虑美尔频率倒谱系数与声纹中心以及噪声之间的关系,并且将声纹特征引入核函数,将原空间样本数据通过非线性变换映射到高维特征空间,在高维空间中求最优或广义最优分类面,实现对语音特征的干扰消除。实验表明,利用改进算法实现了声纹特征中过零率,倒谱特征、矩形窗和汉明窗长的短时能量函数特征的优化。With external noise, identification of voiceprint meyer frequency cepstrum coefficient features various attributes is very vulnerable to external noise interference change, causing low voiceprint characteristics of the recog- nition accuracy. For this, based on support vector machine （ SVM）, a Mel frequency cepstrnm coefficient character- istics interference removal algorithm was proposed. The algorithm in determining the classification decision function fully considers Mel frequency cepstrum coefficient and the relationship between the voice print center and the relation- ship between the noise, and voiceprint features are introduced into the kernel function, and the space sample data through the nonlinear mapping transformation into a high dimensional feature space. In the high dimension space and optimal or generalized optimal classification surface, the speech characteristics of the interference elimination are re- alized. The experimental results show that the use of this kind of algorithm realyzes the optimization of zero - crossing rate of voiceprint, the spectrum characteristics, the rectangular window and hamming window long short - term energy function characteristics.

关 键 词：声音参数 美尔频率倒谱系数 支持向量机 

分 类 号：TP766[自动化与计算机技术—检测技术与自动化装置]