MFCCs

gmm模型下的语音情感识别系统，GMM只是一个数学模型，只是对数据形态的拟和，但是和你所看到的数据分布存在出入也是正常的，因为用EM估计GMM的那些参数时，一般假设我们所得到的数据是不完备的（也就是说假设我们看到的数据分布不是真正的分布，它在运算时把那部分丢失或者叫隐藏的数据“补”上了）(gmm model speech emotion recognition system, GMM is a mathematical model, but fitting the data form, but you can see there is access to the data distribution is normal, because those with the EM estimated GMM parameters, the general assumption we get the data is incomplete (that is, assuming we see the distribution of the data distribution is not real, it is part of the operation at the time that is lost or hidden data is called " fill" on the))

kalman语音处理的源代码，用处语音增强的处理。(the kalman voice processing source code)

c#写的语音识别程序，识别效率还算可以。且根据识别可以给出相应的识别百分比。适合研究语音识别以及tts的参考！(c# to write the speech recognition process, identify efficiency figure. And recognition can be given according to the percentage of the corresponding identification. Tts speech recognition, as well as for research reference!)

模式识别k聚类算法源代码，c语言内容描述。(Pattern Recognition Chapter )

这 里主要对LMS算法及一些改进的LMS算法（NLMS算法、变步长LMS算法、变换域LMS算法）之间的不同点进行了比较，在传统的LMS算法的基础上发 展了LMS算法的应用。另一方面又从RLS算法的分析中对其与LMS算法的不同特性进行了比较。(Here mainly on the LMS algorithm and some improvements of the LMS algorithm (NLMS algorithm, variable step size LMS algorithm, transform domain LMS algorithm) between the different points of comparison, in the traditional LMS algorithm developed on the basis of the application of the LMS algorithm. On the other hand from the analysis of RLS algorithm and LMS algorithm for its different characteristics compared.)

小波去噪，应用于人的语音信号处理中。包含有程序以及运行的结果。(wavelet,denoise)

语音信号的盲源分离算法研究及应用，本程序利用matlab实现(Blind Source Separation Algorithm Research and Application of the voice signal, the program using matlab realize)

高斯混合模型参数初始化程序，在对高斯混合模型的建立之前采用KMEANS算法进行初始化(Gaussian mixture model parameter initialization procedure, in the Gaussian mixture model is initialized before the algorithm used KMEANS)

语音识别,有GUI界面，实现0~9数字语音识别(speaker identification)

1.单通道语音增强算法； 2.一种能够快速跟踪噪声变化的，基于噪声统计特性的噪声估计算法，结合谱减法进行消除噪声； 3.算法复杂度适中，可以满足实时性(1. Single-channel speech enhancement algorithm 2. A fast-track noise can change, based on the noise statistical properties of the noise estimation algorithm, combined with spectral subtraction to eliminate noise 3. Algorithm moderate complexity to meet the real-time)