speech processing

GMM的说话人识别系统，识别效果90 多(GMM speaker recognition system, the recognition effect more than 90 )

cubica算法performes ICA by diagonalization of third- and fourth-order cumulants simultaneously(cubica algorithm performes ICA by diagonalization of third- and fourth-order cumulants simultaneously)

1.进行含噪语音信号的时频分析 2.设计合适滤波器进行去噪 3.进行去噪后信号的时频分析4.设计一个混响器（用四个梳状滤波器和两个全通滤波器（下图所示））来产生回声（通过一个均衡器(Reverberation)

基于matlab的语言情感识别仿真，可以实现用户注册，识别使用者说话时的喜怒哀乐等情感，人数少时可以实现很高的准确率。(Emotion Recognition based on Matlab language simulation, you can implement user registration, the number of emotions such as feelings, identify the user to speak came from a high accuracy can be achieved.)

采样小波包分解语音信号，分解为3层，并求出分解系数(Speech signal using wavelet packet decomposition，decomposing 6 level，and attain decomposition coefficient)

语音信号处理的lbg算法法在语音信号处理中非常重要 (Lbg of voice signal processing algorithms in speech signal processing is very important)

近场的MUSIC算法，可用于近场噪声源识别(Near-field MUSIC algorithm can be used for near-field noise source identification)

回音消除算法matlab,采用的VIRE的双方通话检测(Matlab algorithms eliminate echoes, the two sides call the VIRE Detection)

基于espeak的一个文本转语音的代码,可以把文本转成WAV文件(basic espaeck to translate txt to wav file)

hmm文件时运用HMM算法实现噪声环境下语音识别的。其中vad.m是端点检测程序；mfcc.m是计算MFCC参数的程序；pdf.m函数是计算给定观察向量对该高斯概率密度函数的输出概率；mixture.m是计算观察向量对于某个HMM状态的输出概率，也就是观察向量对该状态的若干高斯混合元的输出概率的线性组合；getparam.m函数是计算前向概率、后向概率、标定系数等参数；viterbi.m是实现Viterbi算法；baum.m是实现Baum-Welch算法；inithmm.m是初始化参数；train.m是训练程序；main.m是训练程序的脚本文件；recog.m是识别程序。(hmm HMM algorithm file using speech recognition in noisy environments. Which is the endpoint detection process vad.m mfcc.m procedure is to calculate the MFCC parameters pdf.m function is calculated for a given observation vector of the Gaussian probability density function of output probability mixture.m is to calculate the observation vector for a HMM state output probability of observation vector is the number of Gaussian mixture per state output probability of the linear combination getparam.m before the calculation of the probability function, backward probability, calibration coefficients and other parameters viterbi.m is Viterbi algorithm implementation baum.m Baum-Welch algorithm to achieve inithmm.m is the initialization parameters train.m is the training program main.m training program is a script file recog.m is to identify procedures.)