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speaker recognition using MFCC GMM EM

优化小波阈值，对硬阈值和软阈值的改进，通常用于语音信号增强，去噪(Optimization of wavelet threshold, the hard threshold and soft threshold improvement, commonly used in speech signal enhancement and denoise)

采用加权网络中节点强度和权重都是幂率分布的模型，计算两个矩阵之间的欧氏距离，实现了对10个数字音的识别。( Using weighted model nodes in the network strength and weight are power law distribution, Calculation of the Euclidean distance between the two matrices, To achieve the recognition of 10 digital sound.)

3、语音特征提取与分类 首先, 待识别语音转化为电信号后输入识别系统, 经过预处理后用数学方法提取语音特征信号, 提取出的语音特征信号可以看成该段语音的模式。(3, the voice feature extraction and classification First, to be recognized voice into electrical signals input recognition system, after pretreatment with a mathematical method to extract speech characteristic signal extracted speech characteristic signal can be seen as the voice segment model.)

用三种经典的方法对语音信号进行增强，效果还不错并且三种方法的比较(With three classical methods of speech signal enhancement, the results were good and Comparison of Three Methods)

基于VQ的说话人识别系统，在MATLAB环境下实现基于矢量量化的说话人识别系统。在实时录音的情况下，利用该说话人识别系统，对不同的人的1s～7s的语音进行辨识。实现与文本无关的自动说话人确认的实时识别。(speaker recognition system based on vector quantization)

语音识别需要用到的函数，在你的程序中可直接调用工具箱中的函数(Needed by the voice recognition function in your program can directly call the functions in the toolbox)

HHT变换，经验模态分解，时变频率与阻尼的模式提取(HHT transform, empirical mode decomposition, extraction of time-varying model)

hMM的前向算法 ,详细的的介绍了算法的过程，并又程序的编程说明(hmm the method of hmm )

语音增强算法综述，还可以，基本的谱减法，维纳滤波法都有(Overview of speech enhancement algorithm, can also basic spectral subtraction, Wiener filtering method has)