このグラフは、どのファイルから直接、間接的にインクルードされているかを示しています。
マクロ定義 | |
| #define | TMBEAMWIDTH 5.0 |
| Score beam offset for GPRUNE_SEL_BEAM. | |
列挙型 | |
| enum | { GPRUNE_SEL_UNDEF, GPRUNE_SEL_NONE, GPRUNE_SEL_SAFE, GPRUNE_SEL_HEURISTIC, GPRUNE_SEL_BEAM } |
| Symbols to specify which Gaussian pruning algorithm to use. [詳細] | |
関数 | |
| int | cache_push (int id, LOGPROB score, int len) |
| Store a score to the current list of computed Gaussians. | |
| LOGPROB | compute_g_base (HTK_HMM_Dens *binfo) |
| boolean | gprune_none_init () |
| void | gprune_none (HTK_HMM_Dens **g, int num, int *last_id) |
| Compute a set of Gaussians with no pruning. | |
| LOGPROB | compute_g_safe (HTK_HMM_Dens *binfo, LOGPROB thres) |
| Calculate probability with safe pruning. | |
| boolean | gprune_safe_init () |
| void | gprune_safe (HTK_HMM_Dens **g, int gnum, int *last_id) |
| Compute a set of Gaussians with safe pruning. | |
| boolean | gprune_heu_init () |
| void | gprune_heu (HTK_HMM_Dens **g, int gnum, int *last_id) |
| Compute a set of Gaussians with heuristic pruning. | |
| boolean | gprune_beam_init () |
| void | gprune_beam (HTK_HMM_Dens **g, int gnum, int *last_id) |
| Compute a set of Gaussians with beam pruning. | |
gprune.h で定義されています。
|
|
Score beam offset for GPRUNE_SEL_BEAM. Larger value may ease pruning error, but processing may become slower. Smaller value can speed up the acoustic computation, but may cause error. 参照元 set_dimthres(). |
|
|
Symbols to specify which Gaussian pruning algorithm to use.
|
|
||||||||||||||||
|
Store a score to the current list of computed Gaussians. Store the calculated score of a Gaussian to OP_calced_score, with its corresponding mixture id to OP_calced_id. The OP_calced_score and OP_calced_id always holds the (OP_gprune_num)-best scores and ids. If the number of stored Gaussian from start has reached OP_gprune_num and the given score is below the bottom, it will be dropped. Else, the new score will be inserted and the bottom will be dropped from the list. The OP_calced_score will always kept sorted by the scores.
gprune_common.c の 87 行で定義されています。 参照元 gprune_beam(), gprune_heu(), と gprune_safe(). |
|
|
Calculate probability of a Gaussian density against input vector on OP_vec.
gprune_none.c の 58 行で定義されています。 参照元 compute_gs_scores(), gprune_beam(), gprune_heu(), gprune_none(), と gprune_safe(). |
|
|
Initialize and setup work area for Gaussian computation
gprune_none.c の 84 行で定義されています。 参照元 outprob_init(). |
|
||||||||||||||||
|
Compute a set of Gaussians with no pruning. The calculated scores will be stored to OP_calced_score, with its corresponding mixture id to OP_calced_id. The number of calculated mixtures is also stored in OP_calced_num. This can be called from calc_tied_mix() or calc_mix().
gprune_none.c の 110 行で定義されています。 参照元 outprob_init(). |
|
||||||||||||
|
Calculate probability with safe pruning. Calculate probability of a Gaussian toward OP_vec, performing pruning using the scholar threshold.
gprune_safe.c の 77 行で定義されています。 参照元 gprune_beam(), gprune_heu(), と gprune_safe(). |
|
|
Initialize and setup work area for Gaussian pruning by safe algorithm.
gprune_safe.c の 106 行で定義されています。 参照元 outprob_init(). |
|
||||||||||||||||
|
Compute a set of Gaussians with safe pruning. If the N-best mixtures in the previous frame is specified in last_id, They are first computed to set the initial threshold. After that, the rest of the Gaussians will be computed with the thresholds to drop unpromising Gaussians from computation at early stage of likelihood computation. If the computation of a Gaussian reached to the end, the threshold will be updated to always hold the likelihood of current N-best score. The calculated scores will be stored to OP_calced_score, with its corresponding mixture id to OP_calced_id. These are done by calling cache_push(). The number of calculated mixtures is also stored in OP_calced_num. This can be called from calc_tied_mix() or calc_mix().
gprune_safe.c の 142 行で定義されています。 参照元 outprob_init(). |
|
|
Initialize and setup work area for Gaussian pruning by heuristic algorithm.
gprune_heu.c の 235 行で定義されています。 参照元 outprob_init(). |
|
||||||||||||||||
|
Compute a set of Gaussians with heuristic pruning. If the N-best mixtures in the previous frame is specified in last_id, They are first computed to get the maximum value for each dimension. After that, the rest of the Gaussians will be computed using the maximum values as heuristics of uncomputed dimensions to drop unpromising Gaussians from computation at early stage of likelihood computation. If the last_id is not specified (typically at the first frame of the input), a safe pruning as same as one in gprune_safe.c will be applied. The calculated scores will be stored to OP_calced_score, with its corresponding mixture id to OP_calced_id. These are done by calling cache_push(). The number of calculated mixtures is also stored in OP_calced_num. This can be called from calc_tied_mix() or calc_mix().
gprune_heu.c の 275 行で定義されています。 参照元 outprob_init(). |
|
|
Initialize and setup work area for Gaussian pruning by beam algorithm.
gprune_beam.c の 225 行で定義されています。 参照元 outprob_init(). |
|
||||||||||||||||
|
Compute a set of Gaussians with beam pruning. If the N-best mixtures in the previous frame is specified in last_id, They are first computed to set the thresholds for each dimension. After that, the rest of the Gaussians will be computed with those dimension thresholds to drop unpromising Gaussians from computation at early stage of likelihood computation. If the last_id is not specified (typically at the first frame of the input), a safe pruning as same as one in gprune_safe.c will be applied. The calculated scores will be stored to OP_calced_score, with its corresponding mixture id to OP_calced_id. These are done by calling cache_push(). The number of calculated mixtures is also stored in OP_calced_num. This can be called from calc_tied_mix() or calc_mix().
gprune_beam.c の 264 行で定義されています。 参照元 outprob_init(). |
1.4.2