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hmm的c++语言实现
- c++实现HMM，向前向后算法，Viterbi算法，Baum-Welch算法。其中包括用c++定义的HMM数据结构。全部是cpp和h的文件-c achieve HMM, forward backward algorithm, Viterbi algorithm, Baum-Welch algorithm. C including the use of the HMM definition data structure. Cpp all
baum
- 在语音识别的hmm模型中使用matlab对语音信号进行训练的把baum源代码-in Speech Recognition hmm use Matlab model of voice signals training source put BAUM
HMMmodel
- This code implements in C++ a basic left-right hidden Markov model and corresponding Baum-Welch (ML) training algorithm. It is meant as an example of the HMM algorithms described by L.Rabiner (1) and others. Seriou
Hidden_Markov_model_for_automatic_speech_recogniti
- Hidden_Markov_model_for_automatic_speech_recognition This code implements in C++ a basic left-right hidden Markov model and corresponding Baum-Welch (ML) training algorithm. It is meant as an example of the HMM alg
ImageFilterBasedP2DHMTModelinWaveletDomain
- 文章提出了一种基于小波域伪二维隐Markov 树（P2DHMT）的图像的滤波新方法。首先建立了小波域的伪 2DHMT 模型，给出了基于EM、Baum-Welch 等算法的模型参数估计方法；
Baum-Eagon-inequality
- introduce a famous inequality in statistical signal processing. This inequality can be used widely in equalization and communication signal processing
hmm的c++语言实现
- c++实现HMM，向前向后算法，Viterbi算法，Baum-Welch算法。其中包括用c++定义的HMM数据结构。全部是cpp和h的文件-c achieve HMM, forward backward algorithm, Viterbi algorithm, Baum-Welch algorithm. C including the use of the HMM definition data structure. Cpp all
baum
- 在语音识别的hmm模型中使用matlab对语音信号进行训练的把baum源代码-in Speech Recognition hmm use Matlab model of voice signals training source put BAUM
HMMmodel
- This code implements in C++ a basic left-right hidden Markov model and corresponding Baum-Welch (ML) training algorithm. It is meant as an example of the HMM algorithms described by L.Rabiner (1) and others. Seriou
Hidden_Markov_model_for_automatic_speech_recogniti
- Hidden_Markov_model_for_automatic_speech_recognition This code implements in C++ a basic left-right hidden Markov model and corresponding Baum-Welch (ML) training algorithm. It is meant as an example of the HMM alg
Baum-Eagon-inequality
- introduce a famous inequality in statistical signal processing. This inequality can be used widely in equalization and communication signal processing
HMM1guide
- How to use the HMM toolbox HMMs with discrete outputs Maximum likelihood parameter estimation using EM (Baum Welch)
baum-welch
- 该程序实现了Baum-Welch算法，对于通信里面的调制解调有很大帮助，其次，该程序具有通用性。-The program achieved the Baum-Welch algorithm, for the communication modem inside helps a lot, and secondly, the program has the versatility.
EM_baumWelch
- 采用Baum-Welch重估计算法对隐马尔科夫模型进行训练，使得结果达到最优化-Using Baum-Welch re-estimation algorithm for training hidden Markov models, making the results of optimized
HMMforspeechrecogntion
- 一个可执行的HMM语音识别程序例程，实现了对10个数字音的识别程序，包含了HMM语音识别中的分段，MFCC特征提取，Baum-Welch训练，及Viterbi等算法，通过此例程可以很好的理解HMM的算法原理-An executable HMM-based 10 digits speech recogntion program example. this code zip file includes segmentation, MFCC
cdhmm
- 连续隐马尔可夫识别程序。 包含的模块，可以比较完整地进行语言识别。 主要模块： Test.m Train.m viterbi.m baum.m pdf.m recog.m mixture.m mfcc.m -speech recognize using HMM include 11 matlab fuction: Test.m Train.m viterbi.m baum.
baum
- this document is about baum-welch algorithm
hmm
- 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Bau
viabw
- 用viterbi算法和baum-welch训练找到和更新best path-find best path using Viterbi algorithm and Baum-Welch training
baum
- 隐马尔可夫模型算法程序中的baum算法，一般用来调用-HMM algorithm program baum algorithm, generally used to call