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

function hmm = baum(hmm, samples) 
 
mix  = hmm.mix;			%高斯混合 
N    = length(mix);		%HMM状态数 
K    = length(samples);	%语音样本数 
SIZE = size(samples(1).data,2); %参数阶数 
 
% 计算前向, 后向概率矩阵, 考虑多观察序列和下溢问题 
disp('计算样本参数...'); 
for k = 1:K 
    fprintf('%d ',k) 
	param(k) = getparam(hmm, samples(k).data); 
end 
fprintf('\n') 
 
% 重估转移概率矩阵A: trans 
disp('重估转移概率矩阵A...') 
for i = 1:N-1 
	denom = 0; 
	for k = 1:K 
		tmp   = param(k).ksai(:,i,:); 
		denom = denom + sum(tmp(:)); 
	end 
	for j = i:i+1 
		nom = 0; 
		for k = 1:K 
			tmp = param(k).ksai(:,i,j); 
			nom = nom   + sum(tmp(:)); 
		end 
		hmm.trans(i,j) = nom / denom; 
	end 
end 
 
% 重估混合高斯的参数 
disp('重估混合高斯的参数...') 
for l = 1:N 
for j = 1:hmm.M(l) 
	fprintf('%d,%d ',l,j) 
	% 计算各pdf的均值和方差 
	nommean = zeros(1,SIZE);  
	nomvar  = zeros(1,SIZE);  
	denom   = 0; 
	for k = 1:K 
		T = size(samples(k).data,1); 
		for t = 1:T 
			x	    = samples(k).data(t,:); 
			nommean = nommean + param(k).gama(t,l,j) * x; 
			nomvar  = nomvar  + param(k).gama(t,l,j) * (x-mix(l).mean(j,:)).^2; 
			denom   = denom   + param(k).gama(t,l,j); 
		end 
	end 
	hmm.mix(l).mean(j,:) = nommean / denom; 
	hmm.mix(l).var (j,:) = nomvar  / denom; 
 
	% 计算各pdf的权 
	nom   = 0; 
	denom = 0; 
	for k = 1:K 
		tmp = param(k).gama(:,l,j);    nom   = nom   + sum(tmp(:)); 
		tmp = param(k).gama(:,l,:);    denom = denom + sum(tmp(:)); 
	end 
	hmm.mix(l).weight(j) = nom/denom; 
end 
fprintf('\n') 
end