matlab_pitch_tone

科大讯飞语音转文字源码是一个基于科大讯飞的语音转文字的例子源码，小编测试一下表示被着准确率惊呆了，之前小编用过其他的语音搜索类的工具，但是准确率总是不那么满意，用这个例子我试着读了杂志上的一段话，语速还略微快了一些，但是转换出来的文字只有两个字跟原文不一样，但是还是同音字。(IFLYTEK speech text source is a turn based on iFLYTEK speech text examples of the source code, test bit representation of the being accurate rate was shocked, before Xiaobian used other voice search tool, but the accuracy is always not so satisfactory, for this example I try to read a passage in the magazine, speed is slightly faster, but convert text only two words with the original text is not the same, but still homophones.)

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)

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 )

digital speech tutorial(digital speech coding for low bit rate communication systems)

code for voice analysis final code for sapeech recognition

hmm文件时运用HMM算法实现噪声环境下语音识别的。其中vad.m是端点检测程序；mfcc.m是计算MFCC参数的程序；pdf.m函数是计算给定观察向量对该高斯概率密度函数的输出概率；mixture.m是计算观察向量对于某个HMM状态的输出概率，也就是观察向量对该状态的若干高斯混合元的输出概率的线性组合；getparam.m函数是计算前向概率、后向概率、标定系数等参数；viterbi.m是实现Viterbi算法；baum.m是实现Baum-Welch算法；inithmm.m是初始化参数；train.m是训练程序；main.m是训练程序的脚本文件；recog.m是识别程序。(hmm HMM algorithm file using speech recognition in noisy environments. Which is the endpoint detection process vad.m mfcc.m procedure is to calculate the MFCC parameters pdf.m function is calculated for a given observation vector of the Gaussian probability density function of output probability mixture.m is to calculate the observation vector for a HMM state output probability of observation vector is the number of Gaussian mixture per state output probability of the linear combination getparam.m before the calculation of the probability function, backward probability, calibration coefficients and other parameters viterbi.m is Viterbi algorithm implementation baum.m Baum-Welch algorithm to achieve inithmm.m is the initialization parameters train.m is the training program main.m training program is a script file recog.m is to identify procedures.)

很好的一本关于语音信号处理的书，而且是中文版的，比较好用(A good book about the processing of speech signal)

微软研究院的说话人识别工具包，包括GMM-UBM、I-Vector。其中demo_gmm_ubm_artificial.m和demo_ivector_plda_artificial.m为生成模拟特征参数进行训练与识别的教学示例，十分适合初学者学习说话人识别基础算法。具体使用方法请看内部文档。(Microsoft Research s speaker recognition toolkit, including GMM-UBM, I-Vector. Demo_gmm_ubm_artificial.m and demo_ivector_plda_artificial.m which generates an analog characteristic parameters for example teaching training and recognition, very suitable for beginners to learn the basic algorithm for speaker recognition. See the specific use of internal documents.)

Matlab 语音合成，可进行语音处理与还原(Matlab dealing with voice)

基于矢量量化的孤立词语音识别系统看，性能良好，Matlab 实现！(Based on vector quantization of isolated word speech recognition system to see, good performance, Matlab to achieve!)