这是一个字符识别程序，主要用在代码和代码之间。

研究生课程：语音识别(吴善培)- Graduate student curriculum: Speech recognition (Wu Shanpei)

本论文研究了窗宽和窗形状对语音信号能量的影响-thesis of the window width and shape of the window of voice signal energy impact

基于WINDOWS的SVM的开发工具，特别适合初学者，不需要太了解SVM原理-SVM based on the Windows development tool, especially for beginners, SVM not understand much about Principle

生成《matlab扩展编程》中对于HMM训练的samples.mat程序，自己编写-Generation ‘matlab programming expansion’ in the HMM training samples.mat procedures, written by myself

G.729协议语音编解码 G.729协议语音编解码 -G.729 Voice codec

一个语谱分析的例子-A spectral analysis of the examples of language

匈牙利算法的实现，比较简单，本人自己写，望大家赐教-Hungarian algorithm is simpler, I wrote it myself, hope everybody with us

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

C50_melp.tar.gz为美军2400bps语音压缩编码算法，该压缩文件是算法在TI的TMS320V5X芯片上的实时实现算法。-C50_melp.tar.gz for U.S. 2400 voice compression encoding algorithm, the document compression algorithm is TMS320V5X in TI"s chip real-time algorithm.

完成基于MFCC的语音识别系统仿真，文件中带有完成语音识别训练的源文件-Completion of MFCC-based speech recognition system simulation, document the completion of speech recognition training with the source file