# parameter file for Viterbi training
#
  
# define training mode (normal, batch or combine)
# - in normal mode, the parameters are updated on the given input data
# - in batch mode, accumulator files are output but no parameter updating
#   is done. combine mode must be used to combine the accumulator files and
#   perform the reestimation
# - in combine mode, accumulator files are combined and used to update the
#   HMM parameters
#
train_mode = normal
# train_mode = combine
# train_mode = batch

# define accumulator file 
#
# batch_stats_file = 

# in combine mode you define an accumulator list containing all of the 
# accumulator filenames produced in batch mode
#
# acc_list_file = acc/acc.list
# define input file list
#
input_file = models/mfc.list

# define output file list of best paths (if this is not set then no
# output will be written)
#
output_file =

# define the transcript file and the type of transcription mode.
# options are word_mlf and model_mlf
#
mlf_file = models/model_label.mlf
mlf_mode = model_mlf

# define the file containing the list of context-independent models
#
monophones_file = models/monophones.text

# define the file containing a list of models which are not expanded
# for context-dependency. typically these are silence models
#
special_model_list = models/special_models.text

# define the model definition file
#
models_file = models/models.text

# define model mapping file created by create_triphone_map
#
phones_file = models/hmm_train_phones.text

# define lexicon file
#
lexicon_file = models/hmm_train_lexicon.text

# define the input states file
#
states_file = models/states.bin

# define the input transitions file
#
transitions_file = models/transitions.text

# in combine mode or normal mode we define the states file which will 
# contain the updated HMM states
#
updated_states_file = models/new_states.text

# in combine mode or normal mode we define the transitions file which will
# contain the updated HMM transitions
#
updated_trans_file = models/new_trans.text

# define alignment mode : model or state (always use state for training)
#
align_mode = state

# define input(mfcc)/output(states) mode file format (ascii or binary)
# accumulator files are always written in binary
#
output_mode = binary

# define context mode (monophone, word_internal, or cross_word)
# 
context_mode = monophone

# define parameters for Viterbi decoding
# - n-best determines how many paths should be kept at each word-end
# - context_size specifies the size of context expansion (3 = triphones)
# - beam_width specifies the beam pruning thresholds (state, model, word)
# - max_active_phones specifies the maximum number of active phone instances
#
n-best = 1
context_size = 1
beam_width = 200 100 100
max_active_phones = 1000
