cs_bp

用vq算法进行说话人识别，能达到良好的效果(Using VQ speaker recognition algorithm can achieve good results)

实现语音识别，使用HTK工具。README描述了具体的训练过程。(Voice recognition, using HTK tools. README describes the specific training process.)

DSP codes,Radial Basis functions, sum of sinusoidal signals,Histogram

3、语音特征提取与分类 首先, 待识别语音转化为电信号后输入识别系统, 经过预处理后用数学方法提取语音特征信号, 提取出的语音特征信号可以看成该段语音的模式。(3, the voice feature extraction and classification First, to be recognized voice into electrical signals input recognition system, after pretreatment with a mathematical method to extract speech characteristic signal extracted speech characteristic signal can be seen as the voice segment model.)

男女声识别程序，中科院数字信号处理第二次作业，分辨率百分之90 以上，带GUI，在前人基础上修改，保证能用，不能用联系我(Male and female vocal recognition program, digital signal processing operations of the Chinese Academy of Sciences, resolution of more than 90 , with a GUI, modify their predecessors on the basis of guaranteed to not use)

国外著名的语音合成开发教程。英文的，原汁原味。(famous speech synthesis Development Guide. English, what is genuine.)

这里面的代码是2DPCA的。这篇文章发表在PAMI上,Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition.google搜索已经引用681次。这里的代码是原作者的。这个分类不太好，语音识别是最近的，这是模式识别里的经典算法。(matlab code of 2DPCA)

MFCC特征提取实例MFCC特征提取实例MFCC特征提取实例MFCC特征提取实例(Examples MFCC feature extraction)

通常语音信号在增强时会出现混响现象，演讲者为了消除背景混响，不得不频繁地偏转头部的方向，这样会造成脉冲响应的不断改变。我们结合盲解卷法和频谱消去法来提高逆滤波器的滤波效果。我们利用输入语音信号间的相关系数矩阵计算出稳定、精确的室内脉冲响应的逆滤波器，而这些输入信号无需测量室内的脉冲响应就能被观测到。逆滤波能够消除早期的反射，这些反射包含混响中的绝大部分能量。之后，用频谱消去法来抑制逆滤波后的信号的尾部混响。本方法在实际适应性方面的表现通过具体的实验进行了验证，结果表明盲解卷法和频谱消去法的结合相较于单独使用一种方法，能够提供一个更优越的演说环境。(Usually voice signal will be enhanced when the reverberation phenomenon, the speaker in order to eliminate the background reverberation, and had to frequently deflect the direction of the head, this will cause the impulse response of changing. We combine the blind deconvolution and spectral elimination method to improve the filtering effect of the inverse filter. We use the correlation coefficient matrix between the input speech signal to calculate the stable, accurate inverse filter of the indoor impulse response, and these input signals do not need to measure the room impulse response can be observed. Inverse filtering to eliminate early reflections, these reflections contains most of the energy in the reverberation. After the spectrum elimination method to suppress the tail reverberation of the signal after the inverse filtering. Performance in the actual adaptation of the method by specific experimental validation results show that the blind deconvolution and spectrum to eliminate )

在MATLAB中完成对语音的梅尔倒谱系数的提取(Extraction of Mel cepstrum coefficients)