56 lines
1.6 KiB
Plaintext
56 lines
1.6 KiB
Plaintext
/**
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@page viterbi_manual viterbi
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@brief *Combine n-gram model and likelihoods to estimate posterior probabilities*
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@tableofcontents
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@section synopsis Synopsis
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@SYNOPSIS@
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`viterbi` is a simple time-synchronous Viterbi decoder. It finds the
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most likely sequence of items drawn from a fixed vocabulary, given
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frame-by-frame observation probabilities for each item in that
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vocabulary, and a ngram grammar. Possible uses include:
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- Simple speech recogniser back end
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`viterbi` can optionally use two sets of frame-by-frame observation
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probabilities in a weighted-sum fashion. Also, the ngram language model
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is not restricted to the conventional sliding window type in which the
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previous n-1 items are the ngram context. Items in the ngram context
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at each frame may be given. In this case, the user must provide a file
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containing the ngram context: one (n-1) tuple per line. To include
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items from the partial Viterbi path so far (i.e. found at recognition
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time, not given) the special notation `<-N>` is used where N indicates
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the distance back to the item required. For example `<-1>` would
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indicate the item on the partial Viterbi path at the last frame. See
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\ref viterbi-examples.
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**Pruning**
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Three types of pruning are available to reduce the size of the search
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space and therefore speed up the search:
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- Observation pruning
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- Top-N pruning at each frame
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- Fixed width beam pruning
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@section options Options
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@OPTIONS@
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@section viterbi-examples Examples
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Example 'given' file (items f and g are in the vocabulary), the ngram
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is a 4-gram.
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<-2> g g
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<-1> g f
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<-1> f g
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<-2> g g
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<-3> g g
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<-1> g f
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*/
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