freetts-1.2-bin

自己认真修改的程序代码，并且通过测试可以运行的！对HMM-GMM的初学者有用。(Own serious modify the program code, and the test can be run. HMM-GMM useful for beginners.)

可对一段信号进行分帧加窗，利用过零率和短时能量检测语音端点(It can divide the frame into a window and use the zero crossing rate and short time energy to detect speech endpoints.)

短时自相关和短时平均幅度差计算基音周期的matlab程序，附带有原语音信号(Short-term autocorrelation and short-term average deviation of pitch period matlab program, comes with the original speech signal)

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.)

房间脉冲响应函数IMAGE,用于计算房间混响时间的(Room impulse response function IMAGE, used to calculate the room reverberation time)

用三种经典的方法对语音信号进行增强，效果还不错并且三种方法的比较(With three classical methods of speech signal enhancement, the results were good and Comparison of Three Methods)

基于最小均方误差的助听器语音去噪算法, 用于以最大程度消除助听器接受的语音杂音.(Speech denoising algorithm based on the minimum mean square error of a hearing aid for the hearing aid acceptable voice murmur eliminated to the greatest extent.)

基于DTW算法实现的语音识别程序，可以识别数字0 9，以及“正弦波，方波，三角波”，并将波形识别结果装入TXT文件中！(DTW algorithm based on speech recognition program that can recognize digits 0 to 9, and sine, square, triangle, and waveform recognition result is loaded into a TXT file!)

System Time Modifier ModifySysTimeItem

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 )