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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 algorithms described by L.Rabiner
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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.
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this document is about baum-welch algorithm
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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
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隐马尔可夫算法的实现，其中包括前向算法，后向算法，Viterbi算法，Baum-Welch算法-Hidden Markov algorithm, including backward algorithm, Viterbi algorithm, Baum-Welch algorithm to the algorithm,
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了解隐马尔科夫模型HMM的概念、组成和需要解决的问题；通过matlab分析和三个基本算法分析读研和就业问题：forward算法、Viterbi算法和Baum-Welch算法-Understand the concept of Hidden Markov Model HMM, composition and problems to be solved matlab analysis by three basic algorithms: forward algorithm, Viterbi alg
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了解隐马尔科夫模型HMM的概念、组成和需要解决的问题；掌握三个基本算法：forward算法、Viterbi算法和Baum-Welch算法，并利用matlab进行实验分析一道具体问题-Understand the concept of Hidden Markov Model HMM, composition and problems to be solved mastered three basic algorithms: forward algorithm, Viterbi algorithm
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用Baum-Welch算法来迭代估计一个隐马尔科夫模型（HMM）的初始状态概率分布以及其状态转移概率矩阵。其中文件有mainfile_B_W.m为主函数，Baum_Welch.m为Baum-Welch算法迭代函数，Forward_variable.m与Backward_variable.m与Gamma_variable.m与Ksi_variable.m是需要计算的四种因子，B_pdf.m为混淆散射概率密度函数。-It s Baum-Welch algorithm for iteratively
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%函数名称：HMMTrain
%参数：V-------训练观察序列（n X 1Cell矩阵），IPI,IA,IB-------模型参数初始值
%返回值：PI,A,B-------模型参数的学习结果
%函数功能：隐含马尔科夫模型的Baum-Welch学习算法(% function name: HMMTrain
% parameters: V------- trains the observation sequence (n, X, 1Cell matrix), IPI, IA, and I
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离散HMM的matlab程序，包含有前向—后向算法、 Baum-Welch算法以及Vertebi算法(The matlab program of discrete HMM, including forward-backward algorithm, Baum-Welch algorithm and Vertebi algorithm)
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