/** * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * **
* Routine for Viterbi decoding.
*
* @param graph HMM/graph to operate on.
* @param gmmProbs Matrix of log prob for each GMM for each frame.
* @param chart Dynamic programming chart to fill in; already
* allocated to be of correct size and initialized with default values.
* @param outLabelList Indices of decoded output tokens are placed here.
* @param acousWgt Acoustic weight.
* @param doAlign If true, return GMM indices rather than word indices
* in @p outLabelList.
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
double viterbi(const Graph& graph, const matrix<double>& gmmProbs,
matrix<VitCell>& chart, vector<int>& outLabelList, double acousWgt,
bool doAlign)
{
int frmCnt = chart.size1() - 1;
int stateCnt = chart.size2();
// BEGIN_LAB
//
// Input:
// An HMM stored in the object "graph" of type "Graph".
// A matrix of doubles "gmmProbs"
//
// gmmProbs(0 .. (frmCnt - 1), 0 .. (#GMM's - 1))
//
// that stores the log prob of each GMM in "gmmSet"
// for each frame.
//
// Output:
// A matrix "chart" of "VitCell" objects (declaration of
// "VitCell" class above):
//
// chart(0 .. frmCnt, 0 .. stateCnt - 1)
//
// On exit, chart(frmIdx, stateIdx).get_log_prob()
// should be set to the logarithm of the probability
// of the best path from the start state to
// state "stateIdx" given the
// first "frmIdx" frames of observations;
// and chart(frmIdx, stateIdx).get_arc_id() should be set
// to the arc ID for the last arc of this best path (or -1
// if the best path is of length 0).
// If a cell is unreachable from the start state,
// these values should be set to "g_zeroLogProb"
// and -1, respectively, which are what these values
// are initialized to on entry.
// The matrix "chart" has already been initialized to be
// of the correct size.
//
// Notes: "g_zeroLogProb" is a large negative number we use
// to represent "ln 0" instead of the actual
// value negative infinity.
// You can assume there are no skip arcs, i.e.,
// arc.get_gmm() >= 0 for all arcs "arc" in the graph.
// Log probabilities should be base e, i.e., natural
// logarithms.
//
// Here is an example of the syntax for accessing a chart
// cell log prob:
//
// logProb = chart(frmIdx, stateIdx).get_log_prob();
//
// Here is an example of setting the contents of a chart cell:
//
// chart(frmIdx, stateIdx).assign(logProb, arcId);
//
// Fill in Viterbi algorithm here.
//
// The code for calculating the final probability and
// the best path is provided for you below.
// Start Viterbi algorithm
// Allocate a matrix for storing the log probability for the each current state giving the previous state
// G(cur_state,pre_state) = chart(frmIdx-1,pre_state)*transformpro*gmmProb
matrix<double> G;
G.resize(stateCnt,stateCnt);
for(int rowIndex=0; rowIndex<stateCnt; ++rowIndex)
for(int colIndex=0; colIndex<stateCnt; ++colIndex)
G(rowIndex,colIndex) = g_zeroLogProb;
vector<double> maxProb(stateCnt); // store the max value for each row of the G matrix
vector<int> maxIndex(stateCnt); // store the Index of the max element for each row of the G matrix
// Initialize start state i.e. chart(0,(0 .. stateCnt - 1))
for(int stateIdx=0; stateIdx<stateCnt; ++stateIdx)
if(stateIdx==graph.get_start_state())
chart(0,stateIdx).assign(0,-1);
else
chart(0,stateIdx).assign(g_zeroLogProb,-1);
// End
for(int frmIdx=0; frmIdx<frmCnt; ++frmIdx){
for(int stateIdx=0; stateIdx<stateCnt; ++stateIdx){
int arcCnt = graph.get_arc_count(stateIdx); // Get number of outgoing arcs for the stateIdx
int arcId = graph.get_first_arc_id(stateIdx); // Get arc ID of the first outgoing arc
for(int arcIdx=0; arcIdx<arcCnt;++arcIdx)
{
Arc arc;
arcId = graph.get_arc(arcId,arc);
int dstState = arc.get_dst_state(); // get the destination state of the stateIdx
double transProb = arc.get_log_prob();
int gmmId = arc.get_gmm();
double gmmProb = gmmProbs(frmIdx,gmmId);
G(dstState,stateIdx) = chart(frmIdx, stateIdx).get_log_prob() + transProb + gmmProb;
}
}
for(int curStaIdx=0; curStaIdx<stateCnt; ++curStaIdx){
maxProb[curStaIdx] = g_zeroLogProb;
maxIndex[curStaIdx] = -1;
for(int preStaIdx=0; preStaIdx<stateCnt; ++preStaIdx)
{
if(G(curStaIdx,preStaIdx) > maxProb[curStaIdx]){
maxProb[curStaIdx] = G(curStaIdx,preStaIdx);
int arcCnt = graph.get_arc_count(preStaIdx); // Get number of outgoing arcs for the stateIdx
int arcId = graph.get_first_arc_id(preStaIdx); // Get arc ID of the first outgoing arc
for(int arcIdx=0; arcIdx<arcCnt;++arcIdx)
{
Arc arc;
arcId = graph.get_arc(arcId,arc);
int dstState = arc.get_dst_state(); // get the destination state of the stateIdx
if(dstState==curStaIdx)
maxIndex[curStaIdx] = arcId-1;
}
}
}
}
// assign the chart(frmIdx+1,0 .. stateCnt - 1)
for(int stateIdx=0; stateIdx<stateCnt; ++stateIdx){
chart(frmIdx+1,stateIdx).assign(maxProb[stateIdx],maxIndex[stateIdx]);
}
// clear the matrix G and the vectors
for(int rowIndex=0; rowIndex<stateCnt; ++rowIndex)
for(int colIndex=0; colIndex<stateCnt; ++colIndex)
G(rowIndex,colIndex) = g_zeroLogProb;
for(int i=0; i<stateCnt; ++i){
maxProb[i] = g_zeroLogProb;
maxIndex[i] = -1;
}
}
// END_LAB
// The code for calculating the final probability and
// the best path is provided for you.
return viterbi_backtrace(graph, chart, outLabelList, doAlign);
}
