Combine n-gram model and likelihoods to estimate posterior probabilities
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 probabilities for each item in that vocabulary, and a ngram grammar. Possible uses include:
viterbi
can optionally use two sets of frame-by-frame observation probabilities in a weighted-sum fashion. Also, the ngram language model is not restricted to the conventional sliding window type in which the previous n-1 items are the ngram context. Items in the ngram context at each frame may be given. In this case, the user must provide a file containing the ngram context: one (n-1) tuple per line. To include items from the partial Viterbi path so far (i.e. found at recognition time, not given) the special notation <-N>
is used where N indicates the distance back to the item required. For example <-1>
would indicate the item on the partial Viterbi path at the last frame. See Examples.
Pruning
Three types of pruning are available to reduce the size of the search space and therefore speed up the search:
Example 'given' file (items f and g are in the vocabulary), the ngram is a 4-gram.
<-2> g g <-1> g f <-1> f g <-2> g g <-3> g g <-1> g f