Sphinx Guideline version 1

Phase 7: TRANSCRIPT - Checking that all the phones in the transcript are in the phonelist, and
all phones in the phonelist appear at least once.
The training process is organized in a list of successive stages, each of which builds on the results
of the previous one. The first step (01.vector_quantize) constructs a generic model of the acoustic
feature space without distinguishing phones. In the second step (20.ci_hmm), individual phones are
trained without reference to their context. The third step (30.cd_hmm_untied) trains separate models
for every triphone (meaning a phone with a specific left and right context). In the fourth step
(40.buildtrees), these triphones are clustered using decision trees, and in the next step
(45.prunetree), the trees are pruned to produce a set of basic acoustic units known as senones or
tied states. Finally, senone models are trained in the last (50.cd_hmm_tied) step.
If everything is set up correctly, you won't have to run each step manually. You can just use the
RunAll.pl script to run the entire training process:
perl scripts_pl/RunAll.pl
2.5.2 Explanation of script in brief:
training.csh
It will change the name of dict, filler dict, transcription file and wavlist file
to{$database}.dic, {$database}.filler, {$database}_train.transcription, {$database}_train.wavList
in the doc folder itself.
Change the databasename, no. of gaussians and senones in sphinx_train.cfg.template and
change the databasename in initialiseAndTrain.csh
Runs the script scripts_pl/initializeAndTrain.csh
initializeAndTrain.csh
The main aim of this script is to remove any discrepancies present in the dict, filler dict,
transcription and wavList files.
It will create a Dictionary file which contains words only present in the transcription thus
saving time while training.
e.g. in my case the original transcription contained 2671 words while the new dictionary
contained 1467 words.
{$database}.phone file is created which will list the set of phones present in the dictionary.
It will run the script make_feats.pl which will create an .mfc file for each of the wav files
and store them in /hmm/feat/
Training will finally begin by running the script scripts_pl/RunAll.pl
Once the training is completed, the models will be stored in /hmm/model_parameters_{$database}.
If the training is done for 1000 senones and 16 Gaussians then following dir will be present in the
above folder:
${database}_s1000_g16.cd_cont_1000 ${database}_s1000_g16.cd_cont_1000_8
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