GMMTEST

HMM语音识别,包含主要算法,进行语音识别(HMM speech recognition, contains the main algorithm for speech recognition)

文中研究了6种常用数字调制信号识别的特征参数集，并采用决策树判别方法进行分类识别。仿真结果表明，在SNR≥5dB时，识别正确率在99 以上，且当SNR≥20dB时，识别正确率达到100 。其特点是，算法简单，识别正确率高，达到了自动分类识别的目的，并有利于实现识别的实时化。(In this paper, we study the set of characteristic parameters of the six kinds of commonly used digital modulation signal recognition, and decision tree method for classification. The simulation results show that SNR 鈮� 5dB, the correct rate more than 99 , and when SNR 鈮� 20dB, the correct rate of 100 . Which is characterized by simple algorithm to identify the correct rate, to achieve the purpose of automatic classification and recognition, and help to identify real-time.)

这是一个在线签名的程序，使用了AR实现算法和DTW算法(This is an online signature process, using the AR algorithm and DTW algorithm)

AMR-WB+编解码源代码，内含工程文件，可直接运行(AMR-WB+ codec source code, containing the project files can be directly run)

一款语音和成软件,主要是文本转语音，包括英文、普通话等(text change to speak)

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)

基于vc++开发，dtw语音识别,MFCC参数(DTW recognice based on vc++)

从wave中读取数据，通过mfcc提取特征系数。从txt文件中读取基于GMM的分类器，最终识别为那种类型(Read data from the wave through the MFCC feature extraction coefficient. Txt file to read from the GMM-based classifier, and ultimately identified as the type)

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算法的分析析中对其与LMS算法的不同特性进行了比较。 (Here the main difference between the LMS algorithm and improved LMS algorithm (NLMS algorithm, variable step size LMS algorithm, the transform domain LMS algorithm) comparison, the traditional LMS algorithm based on the development of the application of the LMS algorithm . On the other hand and from its different characteristics of the LMS algorithm of the analytical analysis of the RLS algorithm.)

程序功能可以基本上实现基于C语言的G.729A语音算法的解码功能，添加了部分本人的注释和理解，便于读者理解(Program features can basically C language-based decoding algorithm G.729A voice, add some of my notes and understanding, easier to understand)