class BeamSearchDecoder

This class implements a Viterbi decoder with beam search capabilities.

Public Members

[more] Decodes using the input data vectors in 'input_data'
[more] 'n_frames_' is the number of vectors of input data
[more] 'vec_size' is the number of elements in each vector.
[more] features or emission probabilities and 'vec_size' must reflect this
[more] After this function returns, 'num_result_words' contains the number of words
[more] recognised and 'result_words' contains the vocabulary indices of the recognised


Documentation

This class implements a Viterbi decoder with beam search capabilities. A Lexicon and LanguageModel are required at creation time (the LanguageModel is optional). By default, no pruning occurs. Two levels of pruning can be configured - word interior hypothesis pruning and word end hypothesis pruning. The application of language model probabilities can be delayed or performed normally.

o Decodes using the input data vectors in 'input_data'
Decodes using the input data vectors in 'input_data'

o 'n_frames_' is the number of vectors of input data
'n_frames_' is the number of vectors of input data

o 'vec_size' is the number of elements in each vector.
'vec_size' is the number of elements in each vector. The input data can be either

o features or emission probabilities and 'vec_size' must reflect this
features or emission probabilities and 'vec_size' must reflect this

o After this function returns, 'num_result_words' contains the number of words
After this function returns, 'num_result_words' contains the number of words

o recognised and 'result_words' contains the vocabulary indices of the recognised
recognised and 'result_words' contains the vocabulary indices of the recognised


This class has no child classes.
Author:
Darren Moore (moore@idiap.ch)

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