refactor kaldi_decode

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diff --git a/tutorial/howto_pretrain_from_kaldi.rst b/tutorial/howto_pretrain_from_kaldi.rst
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+How to Use a Pretrained nnet Model from Kaldi
+=============================================
+
+:author: Ted Yin (mfy43) <[email protected]>
+:abstract: Instruct on how to pretrain a basic dnn with timit dataset using
+           Kaldi and then convert the pretrained model to nerv format to let
+           NERV finetune. Finally it shows two possible ways to decode the
+           finetuned model in Kaldi framework.
+
+- Locate the egs/timit inside Kaldi trunk directory.
+
+- Configure ``cmd.sh`` and ``path.sh`` according to your machine setting.
+
+- Open the ``run.sh`` and locate the line saying ``exit 0 # From this point
+  you can run Karel's DNN: local/nnet/run_dnn.sh``. Uncomment this line. This
+  is because in this tutorial, we only want to train a basic tri-phone DNN,
+  so we simply don't do MMI training, system combination or fancy things like
+  these.
+
+- Run ``./run.sh`` to start the training stages. After that, we will get
+  tri-phone GMM-HMM trained and the aligned labels. Let's move forward to
+  pretrain a DNN.
+
+- Open ``local/nnet/run_dnn.sh``, there are again several stages. Note that
+  the first stage is what we actually need (pretraining the DNN), since in
+  this tutorial we want to demonstrate how to get the pretrained model from
+  stage 1, replace stage 2 with NERV (finetune per-frame cross-entropy), and
+  decode using the finetuned network. However, here we add a line ``exit 0``
+  after stage 2 to preserve stage 2 in order to compare the NERV result
+  against the standard one (the decode result using finetuned model produced
+  by the original stage 2).
+
+- Run ``local/nnet/run_dnn.sh`` (first two stages).
+- You'll find directory like ``dnn4_pretrain-dbn`` and ``dnn4_pretrain-dbn_dnn`` inside the ``exp/``. They correspond to stage 1 and stage 2 respectively. To use NERV to do stage 2 instead, we need the pretrained network and the global transformation from stage 1:
+  
+  - Check the file ``exp/dnn4_pretrain-dbn/6.dbn`` exists. (pretrained network)
+  - Check the file ``exp/dnn4_pretrain-dbn/tr_splice5_cmvn-g.nnet`` exists. (global transformation)
+  - Run script from ``kaldi_io/tools/convert_from_kaldi_pretrain.sh`` to generate the parameters for the output layer and the script files for training and cross-validation set.
+
+  - The previous conversion commands will automatically give identifiers to the
+    parameters read from the Kaldi network file. The identifiers are like, for
+    example, ``affine0_ltp`` and ``bias0``. These names should correspond to
+    the identifiers used in the declaration of the network. Luckily, this
+    tutorial comes with a written network declaration at
+    ``nerv/examples/timit_baseline2.lua``.
+
+- Copy the file ``nerv/examples/timit_baseline2.lua`` to
+  ``timit_mybaseline.lua``, and change the line containing ``/speechlab`` to
+  your own setting.
+
+- Start the NERV training by ``install/bin/nerv nerv/examples/asr_trainer.lua timit_mybaseline.lua``.
+
+  - ``install/bin/nerv`` is the program which sets up the NERV environment,
+
+  - followed by an argument ``nerv/examples/asr_trainer.lua`` which is the script
+    you actually want to run (the general DNN training scheduler),
+
+  - followed by an argument ``timit_mybaseline.lua`` to the scheduler,
+    specifying the network you want to train and some relevant settings, such
+    as where to find the initialized parameters and learning rate, etc.