Description: 这个是隐markov模型中viterbi算法实现的一个具体实例程序 很好-This is a hidden markov models Viterbi Algorithm in a specific example of good procedures Platform: |
Size: 1024 |
Author:juliamie |
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Description: speech recognition using hidden markov model making it speaker independent
Using Matlab 7.5[2008b] Platform: |
Size: 10031104 |
Author:kani |
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Description: 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. Platform: |
Size: 538624 |
Author:于军 |
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Description: 在MATLAB 环境下利用语音工具箱Voice Box 实现基于连续概率密度隐含马尔科夫模型的汉语语音识别系统-Using Voice- Box a mandarin speech recognition system is realized based on CDHMM in MATLAB environment. Platform: |
Size: 210944 |
Author:江丰安迪 |
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Description: The speech signal for the particular isolated word can be viewed as the one generated using the sequential generating probabilistic model known as hidden Markov
model (HMM). Consider there are n states in the HMM. The particular isolated
speech signal is divided into finite number of frames. Every frame of the speech
signal is assumed to be generated from any one of the n states. Each state is modeled as the multivariate Gaussian density function with the specified mean vector
and the covariance matrix. Let the speech segment for the particular isolated word
is represented as vector S. The vector S is divided into finite number of frames
(say M). The i th frame is represented as Si . Every frame is generated by any of the n
states with the specified probability computed using the corresponding multivariate
Gaussian density model. Platform: |
Size: 787456 |
Author:Khan17
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Description: Correlation diagram shown in detail the time domain and frequency domain, Complete HMM-based speech recognition system, Using matlab to calculate the Mahalanobis distance for the image. Platform: |
Size: 60416 |
Author:王永兰
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Search - speech recognition using hmm in matlab