diff --git a/egs/ami/s5b/RESULTS_ihm b/egs/ami/s5b/RESULTS_ihm
index 484115c3243..44234fc3fd9 100644
--- a/egs/ami/s5b/RESULTS_ihm
+++ b/egs/ami/s5b/RESULTS_ihm
@@ -69,7 +69,7 @@ for d in exp/ihm/chain/tdnn_sp_bi/decode_*; do grep Sum $d/*sc*/*ys | utils/best
 %WER 22.4 | 13098 94476 | 80.4 10.4 9.2 2.8 22.4 54.6 | 0.069 | exp/ihm/chain/tdnn_sp_bi/decode_dev/ascore_10/dev_hires.ctm.filt.sys
 %WER 22.5 | 12643 89974 | 80.0 12.1 7.9 2.6 22.5 52.8 | 0.157 | exp/ihm/chain/tdnn_sp_bi/decode_eval/ascore_10/eval_hires.ctm.filt.sys
 
-# local/chain/multi_condition/run_tdnn.sh --mic ihm &
+# local/chain/multi_condition/run_tdnn.sh --mic ihm
 # cleanup + chain TDNN model + IHM reverberated data
 # for d in exp/ihm/chain_cleaned_rvb/tdnn_sp_bi/decode_*; do grep Sum $d/*sc*/*ys | utils/best_wer.sh; done
 %WER 21.5 | 13098 94486 | 81.8 11.0 7.2 3.3 21.5 54.6 | 0.090 | exp/ihm/chain_cleaned_rvb/tdnn_sp_rvb_bi/decode_dev/ascore_10/dev_hires.ctm.filt.sys
@@ -80,3 +80,10 @@ for d in exp/ihm/chain/tdnn_sp_bi/decode_*; do grep Sum $d/*sc*/*ys | utils/best
 # cleanup + chain TDNN+LSTM model
 %WER 20.8 | 13098 94489 | 82.0 10.0 8.0 2.8 20.8 53.2 | -0.096 | exp/ihm/chain_cleaned/tdnn_lstm1i_sp_bi_ld5/decode_dev/ascore_11/dev_hires.ctm.filt.sys
 %WER 20.7 | 12643 89980 | 81.7 11.5 6.8 2.5 20.7 51.8 | 0.015 | exp/ihm/chain_cleaned/tdnn_lstm1i_sp_bi_ld5/decode_eval/ascore_11/eval_hires.ctm.filt.sys
+
+
+# local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh --mic ihm
+# cleanup + chain TDNN+LSTM model + IHM reverberated data
+%WER 19.4 | 13098 94479 | 83.8 10.0 6.1 3.2 19.4 51.8 | -0.168 | exp/ihm/chain_cleaned_rvb/tdnn_lstm1i_sp_rvb_bi/decode_dev/ascore_10/dev_hires.ctm.filt.sys
+%WER 19.3 | 12643 89977 | 83.3 11.0 5.7 2.6 19.3 49.6 | -0.046 | exp/ihm/chain_cleaned_rvb/tdnn_lstm1i_sp_rvb_bi/decode_eval/ascore_10/eval_hires.ctm.filt.sys
+
diff --git a/egs/ami/s5b/RESULTS_mdm b/egs/ami/s5b/RESULTS_mdm
index 80eb152fc5d..f27da5773ac 100644
--- a/egs/ami/s5b/RESULTS_mdm
+++ b/egs/ami/s5b/RESULTS_mdm
@@ -86,6 +86,13 @@
 
 #  local/chain/tuning/run_tdnn_lstm_1i.sh --mic mdm8 --use-ihm-ali true --train-set train_cleaned  --gmm tri3_cleaned
 #  cleanup + chain TDNN+LSTM model, MDM audio and alignments from IHM data
-# *** best system ***
 %WER 34.6 | 15116 94508 | 69.6 17.6 12.9 4.1 34.6 62.3 | 0.687 | exp/mdm8/chain_cleaned/tdnn_lstm1i_sp_bi_ihmali_ld5/decode_dev/ascore_9/dev_hires_o4.ctm.filt.sys
 %WER 37.1 | 14343 90002 | 66.3 18.8 14.9 3.4 37.1 62.3 | 0.659 | exp/mdm8/chain_cleaned/tdnn_lstm1i_sp_bi_ihmali_ld5/decode_eval/ascore_9/eval_hires_o4.ctm.filt.sys
+
+
+# local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh --mic mdm8 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned
+# cleanup + chain TDNN+LSTM model, MDM original + IHM reverberated data, alignments from IHM data
+# *** best system ***
+%WER 31.8 | 14488 94497 | 71.8 15.4 12.8 3.5 31.8 62.7 | 0.698 | exp/mdm8/chain_cleaned_rvb/tdnn_lstm1i_sp_rvb_bi_ihmali/decode_dev/ascore_10/dev_hires_o4.ctm.filt.sys
+%WER 34.2 | 13832 89953 | 68.8 16.5 14.7 3.0 34.2 62.4 | 0.669 | exp/mdm8/chain_cleaned_rvb/tdnn_lstm1i_sp_rvb_bi_ihmali/decode_eval/ascore_10/eval_hires_o4.ctm.filt.sys
+
diff --git a/egs/ami/s5b/RESULTS_sdm b/egs/ami/s5b/RESULTS_sdm
index 5ff1f934a3f..05b68e5e780 100644
--- a/egs/ami/s5b/RESULTS_sdm
+++ b/egs/ami/s5b/RESULTS_sdm
@@ -79,16 +79,22 @@
 %WER 40.7 | 14549 94520 | 63.6 21.4 15.0 4.3 40.7 66.2 | 0.617 | exp/sdm1/chain/tdnn_sp_bi_ihmali/decode_dev/ascore_8/dev_hires_o4.ctm.filt.sys
 %WER 45.1 | 13296 89971 | 59.1 23.4 17.6 4.2 45.1 69.5 | 0.591 | exp/sdm1/chain/tdnn_sp_bi_ihmali/decode_eval/ascore_8/eval_hires_o4.ctm.filt.sys
 
-# local/chain/multi_condition/run_tdnn.sh --mic sdm1 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned &
+# local/chain/multi_condition/run_tdnn.sh --mic sdm1 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned 
 # cleanup + chain TDNN model, SDM original + IHM reverberated data, alignments from ihm data.
-# *** best system ***
 # for d in exp/sdm1/chain_cleaned_rvb/tdnn_sp_rvb_bi_ihmali/decode_*; do grep Sum $d/*sc*/*ys | utils/best_wer.sh; done
 %WER 38.6 | 14760 94502 | 65.3 19.3 15.4 3.9 38.6 64.9 | 0.599 | exp/sdm1/chain_cleaned_rvb/tdnn_sp_rvb_bi_ihmali/decode_dev/ascore_9/dev_hires_o4.ctm.filt.sys
 %WER 42.7 | 14070 89982 | 60.9 21.0 18.0 3.6 42.7 64.5 | 0.571 | exp/sdm1/chain_cleaned_rvb/tdnn_sp_rvb_bi_ihmali/decode_eval/ascore_9/eval_hires_o4.ctm.filt.sys
 
 
 # local/chain/tuning/run_tdnn_lstm_1i.sh --mic sdm1 --use-ihm-ali true --train-set train_cleaned  --gmm tri3_cleaned
-# cleanup + chain TDNN model, SDM audio + alignments from ihm data.
-# *** best system ***
+# cleanup + chain TDNN+LSTM model, SDM audio + alignments from ihm data.
 %WER 37.6 | 15122 94495 | 66.1 18.7 15.1 3.7 37.6 63.2 | 0.646 | exp/sdm1/chain_cleaned/tdnn_lstm1i_sp_bi_ihmali_ld5/decode_dev/ascore_10/dev_hires_o4.ctm.filt.sys
 %WER 40.9 | 13807 89961 | 62.4 20.0 17.6 3.3 40.9 65.7 | 0.612 | exp/sdm1/chain_cleaned/tdnn_lstm1i_sp_bi_ihmali_ld5/decode_eval/ascore_10/eval_hires_o4.ctm.filt.sys
+
+
+# local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh --mic sdm1 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned 
+# cleanup + chain TDNN+LSTM model, SDM original + IHM reverberated data, alignments from ihm data.
+# *** best system ***
+%WER 34.0 | 14455 94497 | 69.8 17.7 12.5 3.8 34.0 63.9 | 0.675 | exp/sdm1/chain_cleaned_rvb/tdnn_lstm1i_sp_rvb_bi_ihmali/decode_dev/ascore_10/dev_hires_o4.ctm.filt.sys
+%WER 37.5 | 13261 89982 | 65.9 19.3 14.7 3.5 37.5 66.2 | 0.642 | exp/sdm1/chain_cleaned_rvb/tdnn_lstm1i_sp_rvb_bi_ihmali/decode_eval/ascore_10/eval_hires_o4.ctm.filt.sys
+
diff --git a/egs/ami/s5b/local/chain/multi_condition/run_tdnn.sh b/egs/ami/s5b/local/chain/multi_condition/run_tdnn.sh
index 617336236ed..0f6abaf94c1 100755
--- a/egs/ami/s5b/local/chain/multi_condition/run_tdnn.sh
+++ b/egs/ami/s5b/local/chain/multi_condition/run_tdnn.sh
@@ -1,9 +1,9 @@
 #!/bin/bash
 
 # This is a chain-training script with TDNN neural networks.
-# This script is based on local/chain/run_tdnn.sh, but adding
+# This script is based on local/chain/tuning/run_tdnn_1a.sh, but adding
 # the reverberated IHM data into the train set.
-# This script obtains better results on both IHM and SDM tasks.
+# This script obtains better results on IHM, SDM and MDM tasks.
 
 # Please see RESULTS_* for examples of command lines invoking this script.
 
@@ -20,7 +20,7 @@ stage=1
 mic=ihm
 nj=30
 min_seg_len=1.55
-use_ihm_ali=true
+use_ihm_ali=false
 train_set=train_cleaned
 gmm=tri3_cleaned  # the gmm for the target data
 ihm_gmm=tri3_cleaned  # the gmm for the IHM system (if --use-ihm-ali true).
diff --git a/egs/ami/s5b/local/chain/multi_condition/run_tdnn_lstm.sh b/egs/ami/s5b/local/chain/multi_condition/run_tdnn_lstm.sh
new file mode 120000
index 00000000000..8e647598556
--- /dev/null
+++ b/egs/ami/s5b/local/chain/multi_condition/run_tdnn_lstm.sh
@@ -0,0 +1 @@
+tuning/run_tdnn_lstm_1a.sh
\ No newline at end of file
diff --git a/egs/ami/s5b/local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh b/egs/ami/s5b/local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh
new file mode 100755
index 00000000000..2869049843f
--- /dev/null
+++ b/egs/ami/s5b/local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh
@@ -0,0 +1,334 @@
+#!/bin/bash
+
+# This is a chain-training script with TDNN+LSTM neural networks.
+# This script is based on local/chain/tuning/run_tdnn_lstm_1i.sh, but adding
+# the reverberated IHM data into the train set.
+# This script obtains better results on IHM, SDM and MDM tasks.
+
+# Please see RESULTS_* for examples of command lines invoking this script.
+
+# local/chain/multi_condition/run_tdnn_lstm.sh --mic ihm --train-set train_cleaned --gmm tri3_cleaned &
+# local/chain/multi_condition/run_tdnn_lstm.sh --mic sdm1 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned &
+# local/chain/multi_condition/run_tdnn_lstm.sh --mic mdm8 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned &
+
+
+set -e -o pipefail
+
+# First the options that are passed through to run_ivector_common.sh
+# (some of which are also used in this script directly).
+stage=0
+mic=ihm
+nj=30
+min_seg_len=1.55
+use_ihm_ali=false
+train_set=train_cleaned
+gmm=tri3_cleaned  # the gmm for the target data
+ihm_gmm=tri3_cleaned  # the gmm for the IHM system (if --use-ihm-ali true).
+num_threads_ubm=32
+num_data_reps=1
+
+chunk_width=150
+chunk_left_context=40
+chunk_right_context=0
+label_delay=5
+# The rest are configs specific to this script.  Most of the parameters
+# are just hardcoded at this level, in the commands below.
+train_stage=-10
+tree_affix=  # affix for tree directory, e.g. "a" or "b", in case we change the configuration.
+tlstm_affix=1i  #affix for TDNN-LSTM directory, e.g. "a" or "b", in case we change the configuration.
+common_egs_dir=  # you can set this to use previously dumped egs.
+
+
+# decode options
+extra_left_context=50
+frames_per_chunk=
+
+
+# End configuration section.
+echo "$0 $@"  # Print the command line for logging
+
+. ./cmd.sh
+. ./path.sh
+. ./utils/parse_options.sh
+
+if ! $use_ihm_ali; then
+  [ "$mic" != "ihm" ] && \
+    echo "$0: you cannot specify --use-ihm-ali false if the microphone is not ihm." && \
+    exit 1;
+else
+  [ "$mic" == "ihm" ] && \
+    echo "$0: you must specify --use-ihm-ali false if the microphone is ihm." && \
+    exit 1;
+fi
+
+if ! cuda-compiled; then
+  cat <<EOF && exit 1
+This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
+If you want to use GPUs (and have them), go to src/, and configure and make on a machine
+where "nvcc" is installed.
+EOF
+fi
+
+nnet3_affix=_cleaned
+rvb_affix=_rvb
+
+
+if $use_ihm_ali; then
+  gmm_dir=exp/ihm/${ihm_gmm}
+  ali_dir=exp/${mic}/${ihm_gmm}_ali_${train_set}_sp_comb_ihmdata
+  lores_train_data_dir=data/$mic/${train_set}_ihmdata_sp_comb
+  tree_dir=exp/$mic/chain${nnet3_affix}/tree_bi${tree_affix}_ihmdata
+  original_lat_dir=exp/$mic/chain${nnet3_affix}/${ihm_gmm}_${train_set}_sp_comb_lats_ihmdata
+  lat_dir=exp/$mic/chain${nnet3_affix}${rvb_affix}/${ihm_gmm}_${train_set}_sp${rvb_affix}_comb_lats_ihmdata
+  dir=exp/$mic/chain${nnet3_affix}${rvb_affix}/tdnn_lstm${tlstm_affix}_sp${rvb_affix}_bi_ihmali
+  # note: the distinction between when we use the 'ihmdata' suffix versus
+  # 'ihmali' is pretty arbitrary.
+else
+  gmm_dir=exp/${mic}/$gmm
+  ali_dir=exp/${mic}/${gmm}_ali_${train_set}_sp_comb
+  lores_train_data_dir=data/$mic/${train_set}_sp_comb
+  tree_dir=exp/$mic/chain${nnet3_affix}/tree_bi${tree_affix}
+  original_lat_dir=exp/$mic/chain${nnet3_affix}/${gmm}_${train_set}_sp_comb_lats
+  lat_dir=exp/$mic/chain${nnet3_affix}${rvb_affix}/${gmm}_${train_set}_sp${rvb_affix}_comb_lats
+  dir=exp/$mic/chain${nnet3_affix}${rvb_affix}/tdnn_lstm${tlstm_affix}_sp${rvb_affix}_bi
+fi
+
+
+local/nnet3/multi_condition/run_ivector_common.sh --stage $stage \
+                                  --mic $mic \
+                                  --nj $nj \
+                                  --min-seg-len $min_seg_len \
+                                  --train-set $train_set \
+                                  --gmm $gmm \
+                                  --num-threads-ubm $num_threads_ubm \
+                                  --num-data-reps $num_data_reps \
+                                  --nnet3-affix "$nnet3_affix"
+
+
+# Note: the first stage of the following script is stage 8.
+local/nnet3/prepare_lores_feats.sh --stage $stage \
+                                   --mic $mic \
+                                   --nj $nj \
+                                   --min-seg-len $min_seg_len \
+                                   --use-ihm-ali $use_ihm_ali \
+                                   --train-set $train_set
+
+
+train_data_dir=data/$mic/${train_set}_sp${rvb_affix}_hires_comb
+train_ivector_dir=exp/$mic/nnet3${nnet3_affix}${rvb_affix}/ivectors_${train_set}_sp${rvb_affix}_hires_comb
+final_lm=`cat data/local/lm/final_lm`
+LM=$final_lm.pr1-7
+
+
+for f in $gmm_dir/final.mdl $lores_train_data_dir/feats.scp \
+   $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
+done
+
+
+if [ $stage -le 11 ]; then
+  if [ -f $ali_dir/ali.1.gz ]; then
+    echo "$0: alignments in $ali_dir appear to already exist.  Please either remove them "
+    echo " ... or use a later --stage option."
+    exit 1
+  fi
+  echo "$0: aligning perturbed, short-segment-combined ${maybe_ihm}data"
+  steps/align_fmllr.sh --nj $nj --cmd "$train_cmd" \
+     ${lores_train_data_dir} data/lang $gmm_dir $ali_dir
+fi
+
+[ ! -f $ali_dir/ali.1.gz ] && echo  "$0: expected $ali_dir/ali.1.gz to exist" && exit 1
+
+if [ $stage -le 12 ]; then
+  echo "$0: creating lang directory with one state per phone."
+  # Create a version of the lang/ directory that has one state per phone in the
+  # topo file. [note, it really has two states.. the first one is only repeated
+  # once, the second one has zero or more repeats.]
+  if [ -d data/lang_chain ]; then
+    if [ data/lang_chain/L.fst -nt data/lang/L.fst ]; then
+      echo "$0: data/lang_chain already exists, not overwriting it; continuing"
+    else
+      echo "$0: data/lang_chain already exists and seems to be older than data/lang..."
+      echo " ... not sure what to do.  Exiting."
+      exit 1;
+    fi
+  else
+    cp -r data/lang data/lang_chain
+    silphonelist=$(cat data/lang_chain/phones/silence.csl) || exit 1;
+    nonsilphonelist=$(cat data/lang_chain/phones/nonsilence.csl) || exit 1;
+    # Use our special topology... note that later on may have to tune this
+    # topology.
+    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >data/lang_chain/topo
+  fi
+fi
+
+if [ $stage -le 13 ]; then
+  # Get the alignments as lattices (gives the chain training more freedom).
+  # use the same num-jobs as the alignments
+  steps/align_fmllr_lats.sh --nj 100 --cmd "$train_cmd" ${lores_train_data_dir} \
+    data/lang $gmm_dir $original_lat_dir
+  rm $original_lat_dir/fsts.*.gz # save space
+
+  lat_dir_ihmdata=exp/ihm/chain${nnet3_affix}/${gmm}_${train_set}_sp_comb_lats
+
+  mkdir -p $lat_dir/temp/
+  mkdir -p $lat_dir/temp2/
+  lattice-copy "ark:gunzip -c $original_lat_dir/lat.*.gz |" ark,scp:$lat_dir/temp/lats.ark,$lat_dir/temp/lats.scp
+  lattice-copy "ark:gunzip -c $lat_dir_ihmdata/lat.*.gz |" ark,scp:$lat_dir/temp2/lats.ark,$lat_dir/temp2/lats.scp
+
+  # copy the lattices for the reverberated data
+  rm -f $lat_dir/temp/combined_lats.scp
+  touch $lat_dir/temp/combined_lats.scp
+  cat $lat_dir/temp/lats.scp >> $lat_dir/temp/combined_lats.scp
+  for i in `seq 1 $num_data_reps`; do
+    cat $lat_dir/temp2/lats.scp | sed -e "s/^/rev${i}_/" >> $lat_dir/temp/combined_lats.scp
+  done
+  sort -u $lat_dir/temp/combined_lats.scp > $lat_dir/temp/combined_lats_sorted.scp
+
+  lattice-copy scp:$lat_dir/temp/combined_lats_sorted.scp "ark:|gzip -c >$lat_dir/lat.1.gz" || exit 1;
+  echo "1" > $lat_dir/num_jobs
+
+  # copy other files from original lattice dir
+  for f in cmvn_opts final.mdl splice_opts tree; do
+    cp $original_lat_dir/$f $lat_dir/$f
+  done
+fi
+
+
+if [ $stage -le 14 ]; then
+  # Build a tree using our new topology.  We know we have alignments for the
+  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
+  # those.
+  if [ -f $tree_dir/final.mdl ]; then
+    echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
+    exit 1;
+  fi
+  steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
+      --context-opts "--context-width=2 --central-position=1" \
+      --leftmost-questions-truncate -1 \
+      --cmd "$train_cmd" 4200 ${lores_train_data_dir} data/lang_chain $ali_dir $tree_dir
+fi
+
+xent_regularize=0.1
+
+if [ $stage -le 15 ]; then
+  echo "$0: creating neural net configs using the xconfig parser";
+
+  num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
+  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=100 name=ivector
+  input dim=40 name=input
+
+  # please note that it is important to have input layer with the name=input
+  # as the layer immediately preceding the fixed-affine-layer to enable
+  # the use of short notation for the descriptor
+  fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
+
+  # the first splicing is moved before the lda layer, so no splicing here
+  relu-renorm-layer name=tdnn1 dim=1024
+  relu-renorm-layer name=tdnn2 input=Append(-1,0,1) dim=1024
+  relu-renorm-layer name=tdnn3 input=Append(-1,0,1) dim=1024
+
+  # check steps/libs/nnet3/xconfig/lstm.py for the other options and defaults
+  lstmp-layer name=lstm1 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3
+  relu-renorm-layer name=tdnn4 input=Append(-3,0,3) dim=1024
+  relu-renorm-layer name=tdnn5 input=Append(-3,0,3) dim=1024
+  relu-renorm-layer name=tdnn6 input=Append(-3,0,3) dim=1024
+  lstmp-layer name=lstm2 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3
+  relu-renorm-layer name=tdnn7 input=Append(-3,0,3) dim=1024
+  relu-renorm-layer name=tdnn8 input=Append(-3,0,3) dim=1024
+  relu-renorm-layer name=tdnn9 input=Append(-3,0,3) dim=1024
+  lstmp-layer name=lstm3 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3
+
+  ## adding the layers for chain branch
+  output-layer name=output input=lstm3 output-delay=$label_delay include-log-softmax=false dim=$num_targets max-change=1.5
+
+  # adding the layers for xent branch
+  # This block prints the configs for a separate output that will be
+  # trained with a cross-entropy objective in the 'chain' models... this
+  # has the effect of regularizing the hidden parts of the model.  we use
+  # 0.5 / args.xent_regularize as the learning rate factor- the factor of
+  # 0.5 / args.xent_regularize is suitable as it means the xent
+  # final-layer learns at a rate independent of the regularization
+  # constant; and the 0.5 was tuned so as to make the relative progress
+  # similar in the xent and regular final layers.
+  output-layer name=output-xent input=lstm3 output-delay=$label_delay dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5
+
+EOF
+
+  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
+fi
+
+if [ $stage -le 16 ]; then
+  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
+    utils/create_split_dir.pl \
+     /export/b0{5,6,7,8}/$USER/kaldi-data/egs/ami-$(date +'%m_%d_%H_%M')/s5b/$dir/egs/storage $dir/egs/storage
+  fi
+
+ steps/nnet3/chain/train.py --stage $train_stage \
+    --cmd "$decode_cmd" \
+    --feat.online-ivector-dir $train_ivector_dir \
+    --feat.cmvn-opts "--norm-means=false --norm-vars=false" \
+    --chain.xent-regularize $xent_regularize \
+    --chain.leaky-hmm-coefficient 0.1 \
+    --chain.l2-regularize 0.00005 \
+    --chain.apply-deriv-weights false \
+    --chain.lm-opts="--num-extra-lm-states=2000" \
+    --egs.dir "$common_egs_dir" \
+    --egs.opts "--frames-overlap-per-eg 0" \
+    --egs.chunk-width $chunk_width \
+    --egs.chunk-left-context $chunk_left_context \
+    --egs.chunk-right-context $chunk_right_context \
+    --trainer.num-chunk-per-minibatch 64 \
+    --trainer.frames-per-iter 1500000 \
+    --trainer.num-epochs 4 \
+    --trainer.optimization.shrink-value 0.99 \
+    --trainer.optimization.num-jobs-initial 2 \
+    --trainer.optimization.num-jobs-final 12 \
+    --trainer.optimization.initial-effective-lrate 0.001 \
+    --trainer.optimization.final-effective-lrate 0.0001 \
+    --trainer.max-param-change 2.0 \
+    --trainer.deriv-truncate-margin 8 \
+    --cleanup.remove-egs true \
+    --feat-dir $train_data_dir \
+    --tree-dir $tree_dir \
+    --lat-dir $lat_dir \
+    --dir $dir
+fi
+
+
+graph_dir=$dir/graph_${LM}
+if [ $stage -le 17 ]; then
+  # Note: it might appear that this data/lang_chain directory is mismatched, and it is as
+  # far as the 'topo' is concerned, but this script doesn't read the 'topo' from
+  # the lang directory.
+  utils/mkgraph.sh --self-loop-scale 1.0 data/lang_${LM} $dir $graph_dir
+fi
+
+if [ $stage -le 18 ]; then
+  rm $dir/.error 2>/dev/null || true
+
+  [ -z $extra_left_context ] && extra_left_context=$chunk_left_context;
+  [ -z $frames_per_chunk ] && frames_per_chunk=$chunk_width;
+
+  for decode_set in dev eval; do
+      (
+      steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+          --nj $nj --cmd "$decode_cmd" \
+          --extra-left-context $extra_left_context  \
+          --frames-per-chunk "$frames_per_chunk" \
+          --online-ivector-dir exp/$mic/nnet3${nnet3_affix}${rvb_affix}/ivectors_${decode_set}_hires \
+          --scoring-opts "--min-lmwt 5 " \
+         $graph_dir data/$mic/${decode_set}_hires $dir/decode_${decode_set} || exit 1;
+      ) || touch $dir/.error &
+  done
+  wait
+  if [ -f $dir/.error ]; then
+    echo "$0: something went wrong in decoding"
+    exit 1
+  fi
+fi
+exit 0
diff --git a/egs/ami/s5b/local/chain/run_tdnn_lstm.sh b/egs/ami/s5b/local/chain/run_tdnn_lstm.sh
new file mode 120000
index 00000000000..43145248fbd
--- /dev/null
+++ b/egs/ami/s5b/local/chain/run_tdnn_lstm.sh
@@ -0,0 +1 @@
+tuning/run_tdnn_lstm_1i.sh
\ No newline at end of file
diff --git a/egs/aspire/s5/local/chain/tuning/run_tdnn_7b.sh b/egs/aspire/s5/local/chain/tuning/run_tdnn_7b.sh
index d91d016a1f4..4bddb3e5955 100755
--- a/egs/aspire/s5/local/chain/tuning/run_tdnn_7b.sh
+++ b/egs/aspire/s5/local/chain/tuning/run_tdnn_7b.sh
@@ -100,6 +100,7 @@ if [ $stage -le 9 ]; then
     data/train data/train_temp_for_lats
   utils/data/combine_short_segments.sh \
       data/train_temp_for_lats $min_seg_len data/train_min${min_seg_len}
+  steps/compute_cmvn_stats.sh data/train_min${min_seg_len} || exit 1;
 fi
 
 if [ $stage -le 10 ]; then
@@ -112,8 +113,8 @@ if [ $stage -le 10 ]; then
   rm -f $lat_dir/fsts.*.gz # save space
 
   rvb_lat_dir=exp/tri5a_rvb_min${min_seg_len}_lats
-  #mkdir -p $rvb_lat_dir/temp/
-  #lattice-copy "ark:gunzip -c $lat_dir/lat.*.gz |" ark,scp:$rvb_lat_dir/temp/lats.ark,$rvb_lat_dir/temp/lats.scp
+  mkdir -p $rvb_lat_dir/temp/
+  lattice-copy "ark:gunzip -c $lat_dir/lat.*.gz |" ark,scp:$rvb_lat_dir/temp/lats.ark,$rvb_lat_dir/temp/lats.scp
 
   # copy the lattices for the reverberated data
   rm -f $rvb_lat_dir/temp/combined_lats.scp