class SimpleDecoderSpeechHMM

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.

Inheritance:

Public Fields

SpeechHMM* model

The trained SpeechHMM model to decode

Grammar* grammar

the acceptable grammar

WordSeg* wordseg

the object containing target and obtained word sequences

real log_word_entrance_penalty

log word entrance penalty: during viterbi, penalizes large sentences

bool forced_alignment

should we perform forced alignment or real decoding?

int* n_previous_states

for each state, how many previous states

int** previous_states

for each state, the list of previous states

Public Methods

SimpleDecoderSpeechHMM(SpeechHMM* model, Grammar* grammar_)

In order to create a SimpleDecoderSpeechHMM, we need to give a vector of n_models_ HMMs 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

virtual void setDataSet(DataSet* data_)

this method sets the test data set to be used

virtual void logViterbi(Sequence* inputs)

this method redefine the normal logViterbi class with constraint on word entrance

virtual void decode(Sequence* input)

this method returns the sentence associated to the input

virtual void logProbabilities(Sequence* inputs)

this method computes more efficiently the emission probabilities

virtual void setPreviousStates()

prepare structure previous_states and n_previous_states

virtual void prepareTestModel(Sequence* input)

this method prepare the transition graph associated with a given test sentence

virtual int nStatesInGrammar()

this methods returns the number of states in the grammar

Inherited from SpeechHMM:

Public Fields

int n_models

HMM** models

DataSet* data

EMTrainer** model_trainer

MeasurerList* initial_models_trainer_measurers

LexiconInfo* lexicon

Sequence* targets

int add_to_targets

bool** word_transitions

int max_n_states

int* states_to_model_states

int* states_to_model

int* states_to_word

bool phoneme_targets

Public Methods

virtual void prepareTrainModel(Sequence* input)

virtual int addWordToModel(int word, int current_state)

virtual void addConnectionsBetweenWordsToModel(int word, int next_word, int current_state, int next_current_state, real log_n_next)

virtual int nStatesInWord(int word)

virtual int nStatesInWordPronunciation(int word, int pronun)

Inherited from HMM:

Public Fields

int n_states

real prior_transitions

Distribution** states

Distribution** shared_states

real** transitions

real** log_transitions

real** dlog_transitions

real** transitions_acc

Sequence* log_alpha

Sequence* log_beta

Sequence* arg_viterbi

int last_arg_viterbi

Sequence* viterbi_sequence

Sequence* log_probabilities_s

bool initialize

Public Methods

virtual void printTransitions(bool real_values=false, bool transitions_only=false)

virtual void logAlpha(Sequence* inputs)

virtual void logBeta(Sequence* inputs)

Inherited from Distribution:

Public Fields

real log_probability

Sequence* log_probabilities

Public Methods

virtual real logProbability(Sequence* inputs)

virtual real viterbiLogProbability(Sequence* inputs)

virtual real frameLogProbability(int t, real* f_inputs)

virtual real viterbiFrameLogProbability(int t, real* f_inputs)

virtual void eMIterInitialize()

virtual void iterInitialize()

virtual void eMSequenceInitialize(Sequence* inputs)

virtual void sequenceInitialize(Sequence* inputs)

virtual void eMAccPosteriors(Sequence* inputs, real log_posterior)

virtual void frameEMAccPosteriors(int t, real* f_inputs, real log_posterior)

virtual void viterbiAccPosteriors(Sequence* inputs, real log_posterior)

virtual void frameViterbiAccPosteriors(int t, real* f_inputs, real log_posterior)

virtual void eMUpdate()

virtual void update()

virtual void eMForward(Sequence* inputs)

virtual void viterbiForward(Sequence* inputs)

virtual void frameBackward(int t, real* f_inputs, real* beta_, real* f_outputs, real* alpha_)

virtual void viterbiBackward(Sequence* inputs, Sequence* alpha)

virtual void frameDecision(int t, real* decision)

Public Members

Returns the decision of the distribution

Inherited from GradientMachine:

Public Fields

int n_inputs

int n_outputs

Parameters* params

Parameters* der_params

Sequence* beta

Public Methods

virtual void forward(Sequence* inputs)

virtual void backward(Sequence* inputs, Sequence* alpha)

virtual void setPartialBackprop(bool flag=true)

virtual void frameForward(int t, real* f_inputs, real* f_outputs)

virtual void loadXFile(XFile* file)

virtual void saveXFile(XFile* file)

Inherited from Machine:

Public Fields

Sequence* outputs

Public Methods

virtual void reset()

Inherited from Object:

Public Fields

Allocator* allocator

Public Methods

void addOption(const char* name, int size, void* ptr, const char* help="")

void addIOption(const char* name, int* ptr, int init_value, const char* help="")

void addROption(const char* name, real* ptr, real init_value, const char* help="")

void addBOption(const char* name, bool* ptr, bool init_value, const char* help="")

void addOOption(const char* name, Object** ptr, Object* init_value, const char* help="")

void setOption(const char* name, void* ptr)

void setIOption(const char* name, int option)

void setROption(const char* name, real option)

void setBOption(const char* name, bool option)

void setOOption(const char* name, Object* option)

void load(const char* filename)

void save(const char* filename)

void* operator new(size_t size, Allocator* allocator_=NULL)

void* operator new(size_t size, Allocator* allocator_, void* ptr_)

void operator delete(void* ptr)

Documentation

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.

SpeechHMM* model

The trained SpeechHMM model to decode

Grammar* grammar

the acceptable grammar

WordSeg* wordseg

the object containing target and obtained word sequences

real log_word_entrance_penalty

log word entrance penalty: during viterbi, penalizes large sentences

bool forced_alignment

should we perform forced alignment or real decoding?

int* n_previous_states

for each state, how many previous states

int** previous_states

for each state, the list of previous states

SimpleDecoderSpeechHMM(SpeechHMM* model, Grammar* grammar_)

In order to create a SimpleDecoderSpeechHMM, we need to give a vector of n_models_ HMMs 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

virtual void setDataSet(DataSet* data_)

this method sets the test data set to be used

virtual void logViterbi(Sequence* inputs)

this method redefine the normal logViterbi class with constraint on word entrance

virtual void decode(Sequence* input)

this method returns the sentence associated to the input

virtual void logProbabilities(Sequence* inputs)

this method computes more efficiently the emission probabilities

virtual void setPreviousStates()

prepare structure previous_states and n_previous_states

virtual void prepareTestModel(Sequence* input)

this method prepare the transition graph associated with a given test sentence

virtual int nStatesInGrammar()

this methods returns the number of states in the grammar

This class has no child classes.

Author:

Samy Bengio (bengio@idiap.ch)

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