Combine n-gram model and likelihoods to estimate posterior probabilities
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