各种语音识别算法的MATLAB代码

语音信号时频分析，包括语谱图，过零率，自编的自相关，以及最后用了三种方法（短时能量和过零率、谱熵法、Teager算子）进行端点检测，代码完整且测试通过(time and frequency domain analysis of speech signals,including spectrogram,rate of zerocrossing,autocorrelation.and three method of endpoint detection,especially the Teager )

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.)

基于MFCC的利用LPCC做的说话人识别论文(Based on the MFCC the use of LPCC do the speaker identification papers)

实现在单信道语音信号处理中的降噪，用Spectral Subtraction算法实现的，内涵音频文件，代码中包括给纯净音频文件加噪声的部分。(Noise reduction in single-channel speech signal processing, using the Spectral Subtraction algorithm, the connotation of an audio file, the code in the package Including pure audio files and part of the noise.)

通过语音分帧、端点检测、pitch提取、DTW算法，实现歌曲识别(Through voice framing, endpoint detection, pit extraction, DTW algorithm, song recognition is realized)

ADPCM(Adaptive Pluse Coding Modular)便解码器，供大家学习参考(ADPCM (Adaptive Pluse Coding Modular) decoder will be available for everyone to learn the reference)

HHT变换，经验模态分解，时变频率与阻尼的模式提取(HHT transform, empirical mode decomposition, extraction of time-varying model)

11阶自适应滤波器的LMS算法，并且与RLS进行了比较(11 steps LMS adaptive filter algorithm and the RLS were compared)

是信号处理的基础，完整的基于HMM的语音识别系统，外文资料里面的源代码。( Is the basis of the signal processing, Complete HMM-based speech recognition system, Foreign materials inside the source code.)

speaker recognition using MFCC GMM EM