viterbi Combine n-gram model and likelihoods to estimate posterior probabilities

Synopsis

viterbi [observations file] -o [output file] [-ngram string] [-given string] [-vocab string] [-ob_type string] [-lm_floor float] [-lm_scale float] [-ob_floor float] [-ob_scale float] [-prev_tag string] [-prev_prev_tag string] [-last_tag string] [-default_tags ] [-observes2 string] [-ob_floor2 float] [-ob_scale2 float] [-ob_prune float] [-n_prune int] [-prune float] [-trace ]

viterbi is a simple time-synchronous Viterbi decoder. It finds the most likely sequence of items drawn from a fixed vocabulary, given frame-by-frame observation probabilites for each item in that vocabulary, and a ngram grammar. Possible uses include:

• Simple speech recogniser back end

Pruning

Three types of pruning are available to reduce the size of the search space and therefore speed up the search:

• Observation pruning

• Top-N pruning at each frame

• Fixed width beam pruning

Options

-ngram	string Grammar file, required
-given	string ngram left contexts, per frame
-vocab	string File with names of vocabulary, this must be same number as width of observations, required
-ob_type	string Observation type : likelihood .... and change doc"probs" or "logs" (default is "logs") Floor values and scaling (scaling is applied after floor value)
-lm_floor	float LM floor probability
-lm_scale	float LM scale factor factor (applied to log prob)
-ob_floor	float Observations floor probability
-ob_scale	float Observation scale factor (applied to prob or log prob, depending on -ob_type)
-prev_tag	string tag before sentence start
-prev_prev_tag	string all words before 'prev_tag'
-last_tag	string after sentence end
-default_tags	use default tags of !ENTER,!EXIT and !EXIT respectively
-observes2	string second observations (overlays first, ob_type must be same)
-ob_floor2	float
-ob_scale2	float
-ob_prune	float observation pruning beam width (log) probability
-n_prune	int top-n pruning of observations
-prune	float pruning beam width (log) probability
-trace	show details of search as it proceeds

Examples

Example 'given' file (items f and g are in the vocabulary), the ngram is a 4-gram.