params_train.text

这是处理语音信号的程序

# parameter file for Baum Welch 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 = combine
# train_mode = batch
train_mode = normal

# define accumulator file 
#
acc_file = 

# in combine mode you define an accumulator list containing all of the 
# accumulator filenames produced in batch mode
#
acclist_file = 

# define input file list
#
mfcclist_file = models/mfc.list

# define model-level transcription file
#
lablist_file = models/model_trans.text

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

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

# define phone mapping file created by create_triphone_map
#
phones_file = models/phones.text

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

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

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

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

# define training thresholds.
# - min_mpd is the minimum model probability deviance
# - beam_width is the beam width used for beta pruning
# - min_occupancy is a floor on the occupancy probability
# - min_model_count is the minimum number of times a model must occur before
#   parameters of that model are updated
#
min_mpd = 1000
beam_width = 500
min_occupancy = 1.0e-10
min_model_count = 1

# define variance floor file
# 
variance_floor_file = models/vfloor.text

# define the state occupancy file. if this mode is turned on then the
# utility will dump state occupancies at the end of the training run. this 
# is a necessary input to the state tying utility.
# the occupancy mode is either "on" or "off"
#
state_occupancy_file = 
state_occupancy = off