speechrecognitionbasedonhmm

端点检测程序 多频带，熵 谱熵 teger能量 希望大家好好学习好好利用(endpoint detection procedures multiband, entropy spectral entropy teger energy hope that we learn to make good use)

基于HMM的语音识别算法和毕设例程，包括数据。(HMM-based speech recognition algorithm and complete routine, including data.)

利用自相关或平均幅度差提取基音原理，帧长160，帧移80。求能量E、过零率Z和基音周期pitch；采用自相关法或平均幅度差法求取基音周期，基音周期范围（80~400hz）；画出能量、过零率和基音曲线。(Pitch principle is extracted by means of autocorrelation or mean amplitude difference)

频域的模态参数识别方法之一，可以对振动信号进行分析处理，得出结构的固有频率，阻尼比，振型系数，方法很好，程序完整(Frequency-domain modal parameter identification methods that can analyze the vibration signal processing, draw the structure of natural frequencies, damping ratio, mode shape coefficient, very good method, program integrity)

gmm模型下的语音情感识别系统，GMM只是一个数学模型，只是对数据形态的拟和，但是和你所看到的数据分布存在出入也是正常的，因为用EM估计GMM的那些参数时，一般假设我们所得到的数据是不完备的（也就是说假设我们看到的数据分布不是真正的分布，它在运算时把那部分丢失或者叫隐藏的数据“补”上了）(gmm model speech emotion recognition system, GMM is a mathematical model, but fitting the data form, but you can see there is access to the data distribution is normal, because those with the EM estimated GMM parameters, the general assumption we get the data is incomplete (that is, assuming we see the distribution of the data distribution is not real, it is part of the operation at the time that is lost or hidden data is called " fill" on the))

运用HMM进行语音识别之后的孤立词识别，里面函数俱全。可以直接运用，简单方便(Speech recognition with HMM isolated word recognition, which function and taste. Can directly use, simple and convenient)

应用LBG算法对语音信号进行矢量量化。本压缩包共有两个主文件，training.m对训练数据进行处理并得到四个初始码本，quantizing.m对待量化数据进行矢量量化。其余为自编功能函数。(application of voice signal VQ. The compressed into two main documents, training.m right training data to be processed and four of the initial code, quantizing.m treat quantitative data for vector quantization. The remainder self-function.)

基于LPC的语音合成，可以实现变调变速功能，附带注释便于学习。(LPC-based speech synthesis, can achieve modulation speed function, annotated easy to learn.)

speech specgram code

fastICA用于盲源分离，文件中有三个语音文件进行混合后再分离。(FastICA is used for blind source separation, in which three voice files are mixed and then separated.)