gusucode.com > 端点检测和基于DTW和HMM的孤立词识别和连续语音识别 > code/cdhmm/viterbi.m

    function [prob,q] = viterbi(hmm, O)
%Viterbi算法
%输入:
%  hmm -- hmm模型
%  O   -- 输入观察序列, N*D, N为帧数,D为向量维数
%输出:
%  prob -- 输出概率
%  q    -- 状态序列

init  = hmm.init;	%初始概率
trans = hmm.trans;	%转移概率
mix   = hmm.mix;	%高斯混合
N     = hmm.N;		%HMM状态数
T     = size(O,1);	%语音帧数

% 计算log(init);
ind1  = find(init>0);
ind0  = find(init<=0);
init(ind0) = -inf;
init(ind1) = log(init(ind1));

% 计算log(trans);
ind1 = find(trans>0);
ind0 = find(trans<=0);
trans(ind0) = -inf;
trans(ind1) = log(trans(ind1));

% 初始化
delta = zeros(T,N);
fai   = zeros(T,N);
q     = zeros(T,1);

% t=1
x = O(1,:);
for i = 1:N
	delta(1,i) = init(i) + log(mixture(mix(i),x));
end

% t=2:T
for t = 2:T
for j = 1:N
	[delta(t,j) fai(t,j)] = max(delta(t-1,:) + trans(:,j)');
	x = O(t,:);
	delta(t,j) = delta(t,j) + log(mixture(mix(j),x));
end
end

% 最终概率和最后节点
[prob q(T)] = max(delta(T,:));

% 回溯最佳状态路径
for t=T-1:-1:1
	q(t) = fai(t+1,q(t+1));
end