libTLK
1.3.1
|
Defines | |
#define | tL_train_viterbi_hmm(HVTRAINER, COUNTS, C, FEA) tL_train_viterbi_hmm_seg ( HVTRAINER, COUNTS, C, FEA, 0, (FEA)->cols ) |
Trains an HMM from a sample using the Viterbi algorithm. | |
Functions | |
tL_hmm_vtrainer_free (tLHMMVTrainer *hvtrainer) | |
Frees memory. | |
tL_hmm_vtrainer_new (const tLAModel *amodel, char **err) | |
Gets a tLHMMVTrainer. | |
tL_train_viterbi_hmm_seg (tLHMMVTrainer *hvtrainer, tLAMiCounts *counts, const int c, const tLFea *fea, const int begin, const int nframes) | |
Trains an HMM from a sample segment using the Viterbi algorithm. |
#define tL_train_viterbi_hmm | ( | HVTRAINER, | |
COUNTS, | |||
C, | |||
FEA | |||
) | tL_train_viterbi_hmm_seg ( HVTRAINER, COUNTS, C, FEA, 0, (FEA)->cols ) |
Trains an HMM from a sample using the Viterbi algorithm.
This macro is equivalent to tL_train_viterbi_hmm_seg but using all frames contained in fea.
HVTRAINER | The HMM Viterbi trainer. |
COUNTS | Integer counts related to AMODEL. |
C | The index of the HMM to be trained. |
FEA | A feature vector manager, which must contain a sequence of feature vectors of an adequate type for the AMODEL. |
tL_hmm_vtrainer_free | ( | tLHMMVTrainer * | hvtrainer | ) |
Frees memory.
Frees the memory allocated for the HMM Viterbi trainer.
hvtrainer | The HMM Viterbi trainer. |
tL_hmm_vtrainer_new | ( | const tLAModel * | amodel, |
char ** | err | ||
) |
Gets a tLHMMVTrainer.
This function creates an HMM Viterbi trainer used to train HMMs with the Viterbi algorithm. Currently supported types are: See tLHMMInitializer.
amodel | The acoustic model. |
err | Pointer to string variable. If not NULL, an error message is allocated in the variable in case of error. |
tL_train_viterbi_hmm_seg | ( | tLHMMVTrainer * | hvtrainer, |
tLAMiCounts * | counts, | ||
const int | c, | ||
const tLFea * | fea, | ||
const int | begin, | ||
const int | nframes | ||
) |
Trains an HMM from a sample segment using the Viterbi algorithm.
This function finds the maximum probability path for the specified HMM of input frames, which are a segment of the given feature vector sequence, and for each transition used it increases the related count by one. Furthermore, the maximum probability path is used to assign the input frames to states, then, states counts are increased as in tL_init_hmm.
hvtrainer | The HMM Viterbi trainer. |
counts | Integer counts related to amodel. |
c | The index of the HMM to be trained. |
fea | A feature vector manager, which must contain a sequence of feature vectors of an adequate type for the amodel. |
begin | The first input frame. |
nframes | The number of input frames. |