viterbi

Table of Contents

• Examples

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 probabilities for each item in that vocabulary, and a ngram grammar. Possible uses include:

• Simple speech recogniser back end

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:

• 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.

<-2> g g
<-1> g f
<-1> f g
<-2> g g
<-3> g g
<-1> g f