Speech:Spring 2017 Modeling Group


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 * Spring 2017
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 * Systems Group
 * Experiment Group
 * Tools Group
 * Data Group
 * [Modeling Group]

Group Member Logs

 * Jonathan Cleary
 * Vitali Taranto
 * Gregory Tinkham
 * Alexander Turner

March 1
Updated instructions for testing on unseen data.
 * .Run addExp.pl
 * 1. Run a training experiment on training data the way you normally would on training data. The documentation here is confusing as it gives a step to make a train for test data but this should be included in the "run test on decode" instructions.
 * 2. Build the language model as you normally would.
 * 3. Create a new test directory for your unseen data test.
 * 4. Run "makeTest.pl   " Use the -t flag for the 30hr test data. Example: makeTest.pl -t switchboard/30hr 0295/015 0295/016
 * 8. The next step is to then run "genFeats.pl -d"
 * 6. CD into the etc folder.
 * 9. Run "nohup run_decode.pl  &"
 * 10. Transform the decode.log file to hyp.trans file by running "parseDecode.pl decode.log hyp.trans"
 * 11. Copy over the /etc/_train.trans from the training experiment.
 * 12. run "sclite -r _train.trans -h hyp.trans -i swb >> scoring.log"

Running LDA
Instructions for running LDA on unseen data.
 * IMPORTANT: any time you log on, you must make certain that your 'python' is 2.7
 * If it is not, run (export PATH=/usr/local/miniconda/bin:$PATH)


 * Add your experiment to the wiki (addExp.pl)
 * make your train using (makeTrain.pl switchboard 30hr/train), if it doesn't work add (/mnt/main/scripts/user/) to the beginning.
 * alter the sphinx_train.cfg file so that $CFG_LDA_MLLT = 'yes' and $CFG_LDA_DIMENSION = 32
 * train as you normally would.
 * build the LM as you normally would.
 * create a new sub-experiment. Make sure to add it to the wiki.
 * Run "makeTest.pl   " inside the new subexp. Use the -t flag for the 30hr test data.
 * cd into the etc folder.
 * notice the files names "est. ". Change the names to "test. ".
 * run (genFeats.pl -d)
 * run (nohup run_decode_lda.pl  &). notice the "lda".
 * run (parseDecode.pl decode.log hyp.trans)
 * Copy over the /etc/_train.trans from the training experiment.
 * run "sclite -r _train.trans -h hyp.trans -i swb >> scoring.log"