### Tools Must be installed ###
install_pfile_utils.sh

### 300 hours Switchboard Task ###
swbd folder includes the recipes of training DNN/CNN/CNN-LACE based Acoustic Models with SWBD Data Set for ASR system.
In contains
	run_swbd_pdnnlg/  
	pdnnlg/ 
	steps_pdnnlg/ 

Please copy all three folders under Kaldi-trunck/egs/swbd/s5c/ , and follow these steps:
	1) Download/Install Kaldi
	2) Use $KALDI_ROOT/egs/swbd/s5c/run.sh recipe to get GMM-HMMs SAT model (tri4) 
	3) Use run_swbd/pdnnlg/run-dnn.sh recipe to train DNN-HMMs model with fMLLR features, and do decoding
	       run_swbd/pdnnlg/run-dnn-fbank.sh recipe to train DNN-HMMs model with filter-bank features, and do decoding
	       run_swbd/pdnnlg/run-cnn.sh recipe to train CNN-HMMs model with filter-bank features, and do decoding
	       run_swbd/pdnnlg/run-cnn-lacea.sh recipe to train CNN-LACE-HMMs model with filter-bank, and do decoding
	       
### 80 hours Wall Street Journal Task ###
wsj folder includes the recipes of training DNN/CNN/CNN-LACE based Acoustic Models with WSJ Data Set for ASR system
In contains
	run_wsj_pdnnlg/  
	pdnnlg/ 
	steps_pdnnlg/ 

Please copy all three folders under Kaldi-trunck/egs/wsj/s5/ , and follow these steps:
	1) Download/Install Kaldi
	2) Use $KALDI_ROOT/egs/wsj/s5/run.sh recipe to get GMM-HMMs SAT model (tri4b) 
	3) Use run_wsj/pdnnlg/run-dnn.sh recipe to train DNN-HMMs model with fMLLR features, and do decoding
	       run_wsj/pdnnlg/run-dnn-fbank.sh recipe to train DNN-HMMs model with filter-bank featuers, and do decoding
	       run_wsj/pdnnlg/run-cnn.sh recipe to train CNN-HMMs model with filter-bank, and do decoding
	       run_wsj/pdnnlg/run-cnn-lacea.sh recipe to train CNN-LACE-HMMs model with filter-bank, and do decoding
	       run_wsj/pdnnlg/run-densenet.sh recipe to train DENSENET-HMMs model with filter-bank, and do decoding