yuyinshiyupinyufenxi

在matlab平台利用GUI界面实现录音，播放。并对语音信号进行频谱分析，特定人语音识别，混响，混频等基础功能。(On the MATLAB platform, the GUI interface is used to record and play. And the speech signal spectrum analysis, specific speech recognition, reverberation, mixing and other basic functions.)

验证码小识别源码程序，结合易语言位图支持库，调用易语言模块实现验证码识别。(Little recognition source code program , combined with easy language bitmap support library , call easy language module verification code recognition .)

基于hmm的识别程序 识别数字0-9，识别率全对(speech recognition based on hmm number 0-9 all right)

语音识别 安卓源代码 和siri一样的音控软件 轻松掌握其中的原理(Speech recognition Anzhuo Yuan code and siri sound control software easily grasp the principle)

基于倒谱短时部分反映了语音的声道特性，先用汉明窗取一帧语音，然后经变换得到语音倒谱，将倒谱短时部分取出，进行正交反变换后将得到声道的对数谱，即得到语音频谱的包络。将频谱包络和频谱画在一张图上，有很好的对比效果。获取的包络效果十分好。(Based Cepstral partly reflects the short channel characteristics of the speech, first take a Hamming window with a frame of speech, and speech cepstrum obtained by converting the cepstrum short segment out inverse orthogonal transform to obtain channel will of the spectrum, i.e. to obtain the envelope of the speech spectrum. The spectral envelope and spectral painted on a chart, there is a good contrast. Get the envelope effect is very good.)

在仿真语音增强时，往往需要向纯净语音中添加适当的噪声然后作仿真对比，这个程序正是实现这了个功能。(When the speech enhancement is emulation, it is often necessary to add appropriate noise to the pure speech and make a simulation contrast. This program is exactly the function of this program.)

iLBC Speech Coder ANSI-C Source Code WebRtcIlbcfix_CbSearch.h.

语音识别与合成入门2，word或压缩包中内有matlab源码(speech recognition and synthesis 2 entry, word or compressed packages within Matlab FOSS)

关于蚁群聚类算法的matlab实现，对于初学者是很有帮助的呀～～ (On the ant colony clustering algorithm matlab realize, for beginners it is helpful ~ ~)

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.)