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// Copyright (C) 2003--2004 Samy Bengio (bengio@idiap.ch)
//
// This file is part of Torch 3.1.
//
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions
// are met:
// 1. Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
// 2. Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
// 3. The name of the author may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
// IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
// OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
// IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
// INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
// NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
// THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#ifndef SIMPLE_DECODER_SPEECH_HMM_INC
#define SIMPLE_DECODER_SPEECH_HMM_INC
#include "SpeechHMM.h"
#include "Grammar.h"
#include "WordSeg.h"
#include "FrameSeg.h"
namespace Torch {
/** This class implements a special case of Hidden Markov Models that
can be used to do connected word speech recognition for small
vocabulary, using embedded training.
It contains a #SpeechHMM# and a grammar (which states
the legal sentences of the langage).
The decoding is done by creating the whole transition matrix
and hence is not adapted to large vocabulary problems.
@author Samy Bengio (bengio@idiap.ch)
*/
class SimpleDecoderSpeechHMM : public SpeechHMM
{
public:
/// The trained SpeechHMM model to decode
SpeechHMM* model;
/// the acceptable grammar
Grammar* grammar;
/// the object containing target and obtained word sequences
WordSeg* wordseg;
FrameSeg* frameseg;
/// log word entrance penalty: during viterbi, penalizes large sentences
real log_word_entrance_penalty;
/// should we perform forced alignment or real decoding?
bool forced_alignment;
/// for each state, how many previous states
int* n_previous_states;
/// for each state, the list of previous states
int** previous_states;
/** In order to create a SimpleDecoderSpeechHMM, we need to give a vector of #n_models_#
#HMM#s as well as their corresponding name, a lexicon and a grammar,
an optional log_word_entrance_penalty and an optional trainer that can be
used to initialize each model independently.
*/
SimpleDecoderSpeechHMM(SpeechHMM* model, Grammar* grammar_);
/// this method sets the test data set to be used
virtual void setDataSet(DataSet* data_);
/** this method redefine the normal logViterbi class with
constraint on word entrance */
virtual void logViterbi(Sequence* inputs);
/// this method returns the sentence associated to the input
virtual void decode(Sequence* input);
/// this method computes more efficiently the emission probabilities
virtual void logProbabilities(Sequence *inputs);
/// prepare structure previous_states and n_previous_states
virtual void setPreviousStates();
/** this method prepare the transition graph associated with a
given test sentence
*/
virtual void prepareTestModel(Sequence* input);
/// this methods returns the number of states in the grammar
virtual int nStatesInGrammar();
virtual void setMaxNStates(int max_n_states_);
virtual ~SimpleDecoderSpeechHMM();
};
}
#endif
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