speech-signal-processing

基于HMM的语音识别算法和毕设例程，包括数据。(HMM-based speech recognition algorithm and complete routine, including data.)

characteristic parameters for speech recognition on the extraction function

语音信号的语谱图生成函数，function [ ] = spetro(Winsiz, Shift, Base, Mode, Gray)，5个参数分别为帧长、帧移、取值门限（一般设为0）、彩色模式、灰度模式(This is a matlab function for creating speech spectrogram.)

matlab编写的muCVSD语音编码解码器，参数可调，十分方便(matlab prepared muCVSD speech codec parameter adjustable, very convenient)

基于人脸和语音双通道的生物认证算法，已经通过编译，效果较好。(based on human face and voice of biological dual-channel authentication algorithms, compiler has been passed, the result is better.)

功率倒置算法的LMS实现，因为自己遇到了点问题，本人一直在找这个程序，没找到，故自己重新检查把问题发现了，相信如果你要实现的话可能会遇到这个问题祝你好运！(Power inversion algorithm LMS implementation, because they encountered a point problem, I ve been looking for this program, did not find it to re-examine the problem yourself discovered, I believe if you want to achieve, then you may encounter this problem Good luck!)

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))

声纹识别和语音识别，语音识别的模块使用的讯飞云。声纹识别的代码则是参考了Dake前辈的代码。在此提出感谢。(Voiceprint recognition and speech recognition, voice recognition module for use hearing Fei. Voiceprint identification code is a reference to the Dake predecessors code. Presented in this thanks.)

自己认真修改的程序代码，并且通过测试可以运行的！对HMM-GMM的初学者有用。(Own serious modify the program code, and the test can be run. HMM-GMM useful for beginners.)

本程序介绍了一种改进的LMS算法-均一化的LMS算法，与传统的LMS算法比较收敛性更好，并由程序给出了比较图(This procedure introduced an improved LMS algorithm- homogenization of the LMS algorithm, with the traditional LMS algorithm convergence better, by the procedures given comparison)