www.pudn.com > VAD-DTW-HMM.rar > inithmm.m

function hmm = inithmm(samples, M) 
 
K = length(samples);	%语音样本数 
N = length(M);			%状态数 
hmm.N = N; 
hmm.M = M; 
 
% 初始概率矩阵 
hmm.init    = zeros(N,1); 
hmm.init(1) = 1; 
 
% 转移概率矩阵 
hmm.trans=zeros(N,N); 
for i=1:N-1 
	hmm.trans(i,i)   = 0.5; 
	hmm.trans(i,i+1) = 0.5; 
end 
hmm.trans(N,N) = 1; 
 
% 概率密度函数的初始聚类 
% 平均分段 
for k = 1:K 
	T = size(samples(k).data,1); 
	samples(k).segment=floor([1:T/N:T T+1]); 
end 
 
%对属于每个状态的向量进行K均值聚类,得到连续混合正态分布 
for i = 1:N 
	%把相同聚类和相同状态的向量组合到一个向量中 
	vector = []; 
	for k = 1:K 
		seg1 = samples(k).segment(i); 
		seg2 = samples(k).segment(i+1)-1; 
		vector = [vector ; samples(k).data(seg1:seg2,:)]; 
	end 
	mix(i) = getmix(vector, M(i)); 
end 
 
hmm.mix = mix; 
 
function mix = getmix(vector, M) 
 
[mean esq nn] = kmeans(vector,M); 
 
% 计算每个聚类的标准差, 对角阵, 只保存对角线上的元素 
for j = 1:M 
	ind = find(j==nn); 
	tmp = vector(ind,:); 
	var(j,:) = std(tmp); 
end 
 
% 计算每个聚类中的元素数, 归一化为各pdf的权重 
weight = zeros(M,1); 
for j = 1:M 
	weight(j) = sum(find(j==nn)); 
end 
weight = weight/sum(weight); 
 
% 保存结果 
mix.M      = M; 
mix.mean   = mean;		% M*SIZE 
mix.var    = var.^2;	% M*SIZE 
mix.weight = weight;	% M*1