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

function [hmm, pout] = train(samples, M) 
%输入: 
%  samples -- 样本结构 
%  M       -- 为每个状态指定pdf个数,如:[3 3 3 3] 
%输出: 
%  hmm      -- 训练完成后的hmm 
 
K   = length(samples); 
 
% 计算语音参数 
disp('正在计算语音参数'); 
for k = 1:K 
	if isfield(samples(k),'data') & ~isempty(samples(k).data) 
		continue; 
	else 
		samples(k).data = mfcc(samples(k).wave); 
	end 
end 
 
hmm = inithmm(samples, M); 
 
for loop = 1:40 
	fprintf('\n第%d遍训练\n\n',loop) 
	hmm = baum(hmm, samples); 
 
	%计算总输出概率 
	pout(loop)=0; 
	for k = 1:K 
		pout(loop) = pout(loop) + viterbi(hmm, samples(k).data); 
	end 
 
	fprintf('总和输出概率(log)=%d\n', pout(loop)) 
 
	%比较两个HMM的距离 
	if loop>1 
		if abs((pout(loop)-pout(loop-1))/pout(loop)) < 5e-6 
			fprintf('收敛!\n'); 
			return 
		end 
	end 
end 
 
disp('迭代40次仍不收敛, 退出');