name: tie_state
synopsis: tie_state [options] [arguments]

description:  
this utility is used to tie states of context dependent models using a
decision tree clustering approach.

arguments: 

   -p params_file        : parameters file
   -help 		     : prints this message
	
example :

   tie_state -p params.text

params.text:

   # parameters file for state tying using decision tree 
   #
   
   # define the mode 
   # - train: in train mode trees are generated and new states and model files
   #   are created using the occupancy counts from a pass of training
   # - test: in test mode trees are read in and models are created for the 
   #   specified phone. the questions in the decision tree nodes are used to
   #   cluster unseen phones to a node in the tree
   #
   # mode = train
   mode = test
   
   # define context-independent (monophones) file
   # 
   monophone_file = monophones.text

   # define the phone membership file. these are the questions that will
   # be used to cluster phones
   #
   membership_file = isip_questions.text

   # define the output models file
   #
   out_models_file = new_models_train.text
   
   # define input/ouput mode for the states file (ascii or binary)
   #
   input_mode = binary
   output_mode = ascii
   
   # define the debug level
   #
   debug_level = 0
   
   # define the output tree file and the output states file
   #
   out_tree_file = trees.1000.500.text
   out_states_file = new_states.text
   
   # define the input states and input model file
   #
   in_states_file = states.bin
   in_models_file = wint_models.text
   
   # define the models which should not be tied (or which cannot exist in 
   # as a central phone of a context-dependent model. eg. sil, sp)
   #
   special_models_file = special_models.text
   
   # define state occupancy file: this is generally the output of a training
   # pass (see bw_train occupancy_list and hmm_train batch_stats_file)
   #
   occupancy_file = occupancy.list
   
   # define thresholds
   # - the split and merge threshold help determine how deep our tree is
   # - the occupancy threshold can be adjusted to prevent overtraining of the
   #   tree
   # - num_context defines the size of the context dependent model. a value of
   #   1 denotes a phone with a left and right context of 1 (a triphone)
   #
   split_threshold = 1000.0
   merge_threshold = -0.5
   occupancy_threshold = 500
   num_context = 1
   
   # define the phlist and clist file
   #
   in_tree_file = trees.1000.500.text
   in_phlist_file = all_wint_triphones.list
   out_clist_file = clist_test.text


man page: http://www.isip.msstate.edu/projects/speech/education/toolkits/asr/acoustic_models/state_tying/tie_state.html





