这是一个语音程序

N best hypothesis document N best hypothesis as used H-N best hypothesis document N best hypothesis as used HMM

在linux平台下,纯c写的盲信号分离的代码.它采用基于卷积混合的盲信号分离算法,不但可以分离人工合成的混合信号,而且对于真实环境中的卷积混合的语音信号也能够分离.在本程序中,包含了两个测试文件,makefile后便可以直接使用. 另外值得一提的是,压缩包里包含有一些语音处理方面的常用函数.例如fft变换,读取写入wav文件,以及一些常用的一维向量和二维矩阵变换的函数.这些可以直接应用在其他应用程序里去.-in linux platform, net write c Blind Signal Separation code. it is based on convolution of mixed Blind Signal Separation algorithm, not only can be separated from the artificial synthesis of mixed-signal, but for the real environment convolution of mixed voice signals can be separated. in this procedure, the tests included two documents, makefile can be used directly after. Also worth mentioning is that Compression bag containing some of voice processing functions commonly used. fft transform, for example, to read write wav file, and some of the commonly used one-dimensional and two-dimensional vector matrix transformation functions. these can be directly applied to other applications going.

HMM模型在语音识别中的应用，相信对HMM模型感兴趣的语音或图像等专业人员应该明白价值。-It is about Hidden Markov Models (HMM) method in Speech Recognition research. those interested in HMM or speech/image processing should know it s value.

模式识别k-means法与k-l变换做的特征提取-K-means pattern recognition method and kl transform to do the feature extraction

基于matlab的信号FFT实现与分析，通过matlab实现-Matlab-based signal with FFT analysis, to achieve through matlab

一个信号采样程序-A signal sampling project.

一个不错的语音编码MATLAB工具箱,用于语音编码 语音压缩和语音识别!-a good speech coding MATLAB Toolbox for speech coding voice compression and voice recognition!

基于G.711、G.721、G.723协议的语音压缩编码C语言源代码-based on G.711, based on G.721 and G.723 voice compression agreement coding C language source code

配合《Matlab之语音处理与合成工具箱》的源代码库，书很经典，是英文原版。-With

该程序是非特定人语音识别程序，原来在SPCE500A下运行，能识别并应答16个命令，存储空间为128K字节。 //我们将其进行了修改，使其能在SPCE061A下运行，由于存储空间为32K字，只能识别并应答9个命令。 //在这个例程当中，我们示范了如何使用语音识别引擎对8个已经经过训练的命令进行识别。 //第一个命令 "Snoopy"，可以作为使能命令，也就是说使用者只能在该命令之后发出其他命令。 //例如，用户只有在发出 "Snoopy"命令并且听到响应"Snoopy here"之后才能继续下达其他命令 //如"How are you", "I love you",等等。 //当"Snoopy"命令响应之后，程序将等待8秒钟，如果没有其他命令发出， //系统将跳回到第0组，并且再次等待触发命令"Snoopy"。当用户的命令无法被识别时， //程序将响应为"Execuse me?", "I can t hear you!"或 "Say again!"这时用户可以再一次下达语音命令。-the procedures were non-specific voice recognition procedure, the original run in SPCE500A can identify and Response 16 orders, storage space for 128K bytes.// We had to amending it so that it can run in SPCE061A, storage space for 32K words, recognize only nine responses and orders.// This routine, we have demonstrated how to use voice recognition engine has eight pairs of trained orders for identification.// The first order "Jack Champion", as it can make an order, users can only say in the order issued after other orders.// For exampl