LPC-FORMANT-PITCH

kalman语音处理的源代码，用处语音增强的处理。(the kalman voice processing source code)

语音识别，dtw算法，matlab程序 语音识别，dtw算法，matlab程序(Speech recognition, dtw algorithm, matlab program speech recognition, dtw algorithm, matlab program)

一个盲信号分离的卷积方法，适合分离实际卷积混合的模型，希望对初学者有帮助。(Convolution of a blind signal separation method, suitable for separation of the real convolution mixture model, want to help beginners.)

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 )

关于如何使用fpga实现语音识别 最新版本(err)

matlab实现的基于小波变化的语音去噪源码，可以去除语音中的背景噪声。(Matlab based on the wavelet changes in the voice de-noising source, you can remove the background noise in the voice.)

一个能实现对语音信号分帧的STFT和ISTFT的函数(Realize a sub-frame of the speech signal and the STFT of the function ISTFT)

在提供音频的情况下，利用双音多频确定拨号音。(In the provision of audio cases, the use of dual-tone multi-frequency determine the dial tone.)

实验示例是基于语音中的mfcc，语音倒谱特征来进行聚类，先利用训练样本来计算训练样本聚类中心（用到了lbg算法），之后再进行分类。 注意：使用代码时需要自己更改文件路径。(This example is based on the MFCC in speech and the feature of speech Cepstrum to cluster. First, the training sample is used to calculate the training sample clustering center (using the LBG algorithm), then the classification is then carried out.)

用VC++实现的关于语音识别中提取MFCC程序，有界面，程序能运行成功。(Use of the vc of voice recognition mfcc program, the interface, the application can run. )