diff --git a/egs/iam/v1/local/chain/run_cnn_1a.sh b/egs/iam/v1/local/chain/run_cnn_1a.sh
index 05cb9948bd9..e2a6988e6fe 100755
--- a/egs/iam/v1/local/chain/run_cnn_1a.sh
+++ b/egs/iam/v1/local/chain/run_cnn_1a.sh
@@ -89,7 +89,7 @@ if [ $stage -le 1 ]; then
   # 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 $lang ]; then
-    if [ $lang/L.fst -nt data/$lang_test/L.fst ]; then
+    if [ $lang/L.fst -nt data/lang/L.fst ]; then
       echo "$0: $lang already exists, not overwriting it; continuing"
     else
       echo "$0: $lang already exists and seems to be older than data/lang..."
@@ -97,7 +97,7 @@ if [ $stage -le 1 ]; then
       exit 1;
     fi
   else
-    cp -r data/$lang_test $lang
+    cp -r data/lang $lang
     silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
     nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
     # Use our special topology... note that later on may have to tune this
@@ -110,7 +110,7 @@ if [ $stage -le 2 ]; 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 $nj --cmd "$cmd" ${train_data_dir} \
-    data/$lang_test $gmm_dir $lat_dir
+    data/lang $gmm_dir $lat_dir
   rm $lat_dir/fsts.*.gz # save space
 fi
 
diff --git a/egs/iam/v1/local/chain/run_cnn_chainali_1a.sh b/egs/iam/v1/local/chain/run_cnn_chainali_1a.sh
index 2c8b6c91e5a..fc9d09a755b 100755
--- a/egs/iam/v1/local/chain/run_cnn_chainali_1a.sh
+++ b/egs/iam/v1/local/chain/run_cnn_chainali_1a.sh
@@ -2,21 +2,6 @@
 
 # chainali_1a is as 1a except it uses chain alignments (using 1a system) instead of gmm alignments
 
-# ./local/chain/compare_wer.sh exp/chain/cnn_chainali_1a/ exp/chain/cnn_1a/
-# System                      cnn_chainali_1a    cnn_1a
-# WER                             6.69     9.13
-# Final train prob              -0.0128   -0.0297
-# Final valid prob              -0.0447   -0.0975
-# Final train prob (xent)       -0.6448   -0.5915
-# Final valid prob (xent)       -0.9924   -1.0022
-
-# steps/info/chain_dir_info.pl exp/chain/cnn_chainali_1a/
-# exp/chain/cnn_chainali_1a/: num-iters=21 nj=2..4 num-params=4.4M dim=40->364 combine=-0.002->0.000 xent:train/valid[13,20,final]=(-0.929,-0.711,-0.645/-1.16,-1.04,-0.992) logprob:train/valid[13,20,final]=(-0.029,-0.016,-0.013/-0.051,-0.047,-0.045)
-
-# cat exp/chain/cnn_chainali_1a/decode_test/scoring_kaldi/best_*
-# %WER 3.94 [ 2600 / 65921, 549 ins, 837 del, 1214 sub ] exp/chain/cnn_chainali_1a/decode_test/cer_15_0.0
-# %WER 6.69 [ 1241 / 18542, 135 ins, 358 del, 748 sub ] exp/chain/cnn_chainali_1a/decode_test/wer_15_0.5
-
 set -e -o pipefail
 
 stage=0
@@ -28,7 +13,7 @@ gmm=tri3        # this is the source gmm-dir that we'll use for alignments; it
 nnet3_affix=    # affix for exp dirs, e.g. it was _cleaned in tedlium.
 affix=_1a  #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
 ali=tri3_ali
-chain_model_dir=exp/chain${nnet3_affix}/cnn${affix}
+chain_model_dir=exp/chain${nnet3_affix}/cnn_1a
 common_egs_dir=
 reporting_email=
 
@@ -90,7 +75,7 @@ if [ $stage -le 1 ]; then
   # 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 $lang ]; then
-    if [ $lang/L.fst -nt data/$lang_test/L.fst ]; then
+    if [ $lang/L.fst -nt data/lang/L.fst ]; then
       echo "$0: $lang already exists, not overwriting it; continuing"
     else
       echo "$0: $lang already exists and seems to be older than data/lang..."
@@ -98,7 +83,7 @@ if [ $stage -le 1 ]; then
       exit 1;
     fi
   else
-    cp -r data/$lang_test $lang
+    cp -r data/lang $lang
     silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
     nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
     # Use our special topology... note that later on may have to tune this
@@ -112,7 +97,7 @@ if [ $stage -le 2 ]; then
   # use the same num-jobs as the alignments
   steps/nnet3/align_lats.sh --nj $nj --cmd "$cmd" \
                             --scale-opts '--transition-scale=1.0 --self-loop-scale=1.0' \
-                            ${train_data_dir} data/$lang_test $chain_model_dir $lat_dir
+                            ${train_data_dir} data/lang $chain_model_dir $lat_dir
   cp $gmm_lat_dir/splice_opts $lat_dir/splice_opts
 fi
 
diff --git a/egs/iam/v1/local/chain/run_cnn_chainali_1b.sh b/egs/iam/v1/local/chain/run_cnn_chainali_1b.sh
index d6d0ee780f4..389e08ad5ba 100755
--- a/egs/iam/v1/local/chain/run_cnn_chainali_1b.sh
+++ b/egs/iam/v1/local/chain/run_cnn_chainali_1b.sh
@@ -27,7 +27,7 @@ gmm=tri3        # this is the source gmm-dir that we'll use for alignments; it
 nnet3_affix=    # affix for exp dirs, e.g. it was _cleaned in tedlium.
 affix=_1b  #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
 ali=tri3_ali
-chain_model_dir=exp/chain${nnet3_affix}/cnn${affix}
+chain_model_dir=exp/chain${nnet3_affix}/cnn_1a
 common_egs_dir=
 reporting_email=
 
@@ -89,7 +89,7 @@ if [ $stage -le 1 ]; then
   # 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 $lang ]; then
-    if [ $lang/L.fst -nt data/$lang_test/L.fst ]; then
+    if [ $lang/L.fst -nt data/lang/L.fst ]; then
       echo "$0: $lang already exists, not overwriting it; continuing"
     else
       echo "$0: $lang already exists and seems to be older than data/lang..."
@@ -97,7 +97,7 @@ if [ $stage -le 1 ]; then
       exit 1;
     fi
   else
-    cp -r data/$lang_test $lang
+    cp -r data/lang $lang
     silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
     nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
     # Use our special topology... note that later on may have to tune this
@@ -111,7 +111,7 @@ if [ $stage -le 2 ]; then
   # use the same num-jobs as the alignments
   steps/nnet3/align_lats.sh --nj $nj --cmd "$cmd" \
                             --scale-opts '--transition-scale=1.0 --self-loop-scale=1.0' \
-                            ${train_data_dir} data/$lang_test $chain_model_dir $lat_dir
+                            ${train_data_dir} data/lang $chain_model_dir $lat_dir
   cp $gmm_lat_dir/splice_opts $lat_dir/splice_opts
 fi
 
diff --git a/egs/iam/v1/local/chain/run_cnn_chainali_1c.sh b/egs/iam/v1/local/chain/run_cnn_chainali_1c.sh
index 6ff76490303..9cc74093165 100755
--- a/egs/iam/v1/local/chain/run_cnn_chainali_1c.sh
+++ b/egs/iam/v1/local/chain/run_cnn_chainali_1c.sh
@@ -25,7 +25,7 @@ gmm=tri3        # this is the source gmm-dir that we'll use for alignments; it
 nnet3_affix=    # affix for exp dirs, e.g. it was _cleaned in tedlium.
 affix=_1c  #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
 ali=tri3_ali
-chain_model_dir=exp/chain${nnet3_affix}/cnn${affix}
+chain_model_dir=exp/chain${nnet3_affix}/cnn_1a
 common_egs_dir=
 reporting_email=
 
@@ -33,7 +33,6 @@ reporting_email=
 train_stage=-10
 xent_regularize=0.1
 frame_subsampling_factor=4
-alignment_subsampling_factor=1
 # training chunk-options
 chunk_width=340,300,200,100
 num_leaves=500
@@ -75,7 +74,6 @@ tree_dir=exp/chain${nnet3_affix}/tree_chain
 # you should probably name it differently.
 lang=data/lang_chain
 for f in $train_data_dir/feats.scp \
-    $train_data_dir/feats.scp $gmm_dir/final.mdl \
     $ali_dir/ali.1.gz $gmm_dir/final.mdl; do
   [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
 done
@@ -87,7 +85,7 @@ if [ $stage -le 1 ]; then
   # 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 $lang ]; then
-    if [ $lang/L.fst -nt data/$lang_test/L.fst ]; then
+    if [ $lang/L.fst -nt data/lang/L.fst ]; then
       echo "$0: $lang already exists, not overwriting it; continuing"
     else
       echo "$0: $lang already exists and seems to be older than data/lang..."
@@ -95,7 +93,7 @@ if [ $stage -le 1 ]; then
       exit 1;
     fi
   else
-    cp -r data/$lang_test $lang
+    cp -r data/lang $lang
     silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
     nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
     # Use our special topology... note that later on may have to tune this
@@ -109,7 +107,7 @@ if [ $stage -le 2 ]; then
   # use the same num-jobs as the alignments
   steps/nnet3/align_lats.sh --nj $nj --cmd "$cmd" \
                             --scale-opts '--transition-scale=1.0 --self-loop-scale=1.0' \
-                            ${train_data_dir} data/$lang_test $chain_model_dir $lat_dir
+                            ${train_data_dir} data/lang $chain_model_dir $lat_dir
   cp $gmm_lat_dir/splice_opts $lat_dir/splice_opts
 fi
 
@@ -136,12 +134,12 @@ if [ $stage -le 4 ]; then
 
   num_targets=$(tree-info $tree_dir/tree | grep num-pdfs | awk '{print $2}')
   learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
-  opts="l2-regularize=0.075"
-  opts_2="l2-regularize=0.075"
-  opts_3="l2-regularize=0.1"
-  common1="$opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=36"
-  common2="$opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=70"
-  common3="$opts required-time-offsets= height-offsets=-1,0,1 num-filters-out=70"
+  cnn_opts="l2-regularize=0.075"
+  tdnn_opts="l2-regularize=0.075"
+  output_opts="l2-regularize=0.1"
+  common1="$cnn_opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=36"
+  common2="$cnn_opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=70"
+  common3="$cnn_opts required-time-offsets= height-offsets=-1,0,1 num-filters-out=70"
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
   input dim=40 name=input
@@ -153,13 +151,13 @@ if [ $stage -le 4 ]; then
   conv-relu-batchnorm-layer name=cnn5 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2
   conv-relu-batchnorm-layer name=cnn6 height-in=10 height-out=10 time-offsets=-1,0,1 $common3
   conv-relu-batchnorm-layer name=cnn7 height-in=10 height-out=10 time-offsets=-1,0,1 $common3
-  relu-batchnorm-layer name=tdnn1 input=Append(-4,-2,0,2,4) dim=$tdnn_dim $opts_2
-  relu-batchnorm-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim $opts_2
-  relu-batchnorm-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim $opts_2
+  relu-batchnorm-layer name=tdnn1 input=Append(-4,-2,0,2,4) dim=$tdnn_dim $tdnn_opts
+  relu-batchnorm-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim $tdnn_opts
+  relu-batchnorm-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim $tdnn_opts
 
   ## adding the layers for chain branch
-  relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5 $opts_2
-  output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5 $opts_3
+  relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5 $tdnn_opts
+  output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5 $output_opts
 
   # adding the layers for xent branch
   # This block prints the configs for a separate output that will be
@@ -170,8 +168,8 @@ if [ $stage -le 4 ]; then
   # final-layer learns at a rate independent of the regularization
   # constant; and the 0.5 was tuned so as to make the relative progress
   # similar in the xent and regular final layers.
-  relu-batchnorm-layer name=prefinal-xent input=tdnn3 dim=$tdnn_dim target-rms=0.5 $opts_2
-  output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5 $opts_3
+  relu-batchnorm-layer name=prefinal-xent input=tdnn3 dim=$tdnn_dim target-rms=0.5 $tdnn_opts
+  output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5 $output_opts
 EOF
   steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
 fi
@@ -192,7 +190,9 @@ if [ $stage -le 5 ]; then
     --chain.apply-deriv-weights=false \
     --chain.lm-opts="--num-extra-lm-states=500" \
     --chain.frame-subsampling-factor=$frame_subsampling_factor \
-    --chain.alignment-subsampling-factor=$alignment_subsampling_factor \
+    --chain.alignment-subsampling-factor=1 \
+    --chain.left-tolerance 3 \
+    --chain.right-tolerance 3 \
     --trainer.srand=$srand \
     --trainer.max-param-change=2.0 \
     --trainer.num-epochs=4 \
diff --git a/egs/iam/v1/local/chain/run_flatstart_cnn1a.sh b/egs/iam/v1/local/chain/run_flatstart_cnn1a.sh
index 65eeedcc75b..e22fe03899e 100755
--- a/egs/iam/v1/local/chain/run_flatstart_cnn1a.sh
+++ b/egs/iam/v1/local/chain/run_flatstart_cnn1a.sh
@@ -5,16 +5,16 @@
 
 # local/chain/compare_wer.sh exp/chain/cnn_1a exp/chain/cnn_chainali_1c exp/chain/e2e_cnn_1a
 # System                         cnn_1a cnn_chainali_1c e2e_cnn_1a
-# WER                             18.58     12.84     15.46
-# CER                             10.17      6.40      7.21
-# Final train prob              -0.0122   -0.0120   -0.0426
-# Final valid prob              -0.0999   -0.0199   -0.0724
+# WER                             18.58     12.84     14.06
+# CER                             10.17      6.40      6.57
+# Final train prob              -0.0122   -0.0120   -0.0346
+# Final valid prob              -0.0999   -0.0199   -0.0594
 # Final train prob (xent)       -0.5652   -0.9973
 # Final valid prob (xent)       -0.9758   -1.1537
 # Parameters                      4.36M     3.96M     9.13M
 
-# steps/info/chain_dir_info.pl exp/chain/e2e_cnn_1a/
-# exp/chain/e2e_cnn_1a/: num-iters=21 nj=2..4 num-params=9.1M dim=40->12640 combine=-0.040->-0.040 (over 1) logprob:train/valid[13,20,final]=(-0.065,-0.046,-0.043/-0.081,-0.073,-0.072)
+# steps/info/chain_dir_info.pl exp/chain/e2e_cnn_1a
+# exp/chain/e2e_cnn_1a: num-iters=21 nj=2..4 num-params=9.1M dim=40->12640 combine=-0.033->-0.033 (over 1) logprob:train/valid[13,20,final]=(-0.058,-0.042,-0.035/-0.070,-0.064,-0.059)
 
 set -e
 
@@ -34,8 +34,8 @@ common_egs_dir=
 l2_regularize=0.00005
 frames_per_iter=1000000
 cmvn_opts="--norm-means=true --norm-vars=true"
-train_set=train_e2e
-lang_test=lang_test
+train_set=train
+lang_test=lang_unk
 
 # End configuration section.
 echo "$0 $@"  # Print the command line for logging
@@ -74,19 +74,24 @@ if [ $stage -le 1 ]; then
                                        --shared-phones true \
                                        --type biphone \
                                        data/$train_set $lang $treedir
-  cp exp/chain/e2e_base/phone_lm.fst $treedir/
+  $cmd $treedir/log/make_phone_lm.log \
+  cat data/$train_set/text \| \
+    steps/nnet3/chain/e2e/text_to_phones.py data/lang \| \
+    utils/sym2int.pl -f 2- data/lang/phones.txt \| \
+    chain-est-phone-lm --num-extra-lm-states=500 \
+                       ark:- $treedir/phone_lm.fst
 fi
 
 if [ $stage -le 2 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
   num_targets=$(tree-info $treedir/tree | grep num-pdfs | awk '{print $2}')
 
-  opts="l2-regularize=0.075"
-  opts_2="l2-regularize=0.075"
-  opts_3="l2-regularize=0.1"
-  common1="$opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=36"
-  common2="$opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=70"
-  common3="$opts required-time-offsets= height-offsets=-1,0,1 num-filters-out=70"
+  cnn_opts="l2-regularize=0.075"
+  tdnn_opts="l2-regularize=0.075"
+  output_opts="l2-regularize=0.1"
+  common1="$cnn_opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=36"
+  common2="$cnn_opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=70"
+  common3="$cnn_opts required-time-offsets= height-offsets=-1,0,1 num-filters-out=70"
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
   input dim=40 name=input
@@ -98,13 +103,13 @@ if [ $stage -le 2 ]; then
   conv-relu-batchnorm-layer name=cnn5 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2
   conv-relu-batchnorm-layer name=cnn6 height-in=10 height-out=10 time-offsets=-1,0,1 $common3
   conv-relu-batchnorm-layer name=cnn7 height-in=10 height-out=10 time-offsets=-1,0,1 $common3
-  relu-batchnorm-layer name=tdnn1 input=Append(-4,-2,0,2,4) dim=$tdnn_dim $opts_2
-  relu-batchnorm-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim $opts_2
-  relu-batchnorm-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim $opts_2
+  relu-batchnorm-layer name=tdnn1 input=Append(-4,-2,0,2,4) dim=$tdnn_dim $tdnn_opts
+  relu-batchnorm-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim $tdnn_opts
+  relu-batchnorm-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim $tdnn_opts
 
   ## adding the layers for chain branch
-  relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5 $opts_2
-  output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5 $opts_3
+  relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5 $output_opts
+  output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5 $output_opts
 EOF
 
   steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs
diff --git a/egs/iam/v1/local/make_features.py b/egs/iam/v1/local/make_features.py
index 8cfca5ee830..84e012daedb 100755
--- a/egs/iam/v1/local/make_features.py
+++ b/egs/iam/v1/local/make_features.py
@@ -124,4 +124,4 @@ def get_scaled_image(im, allowed_lengths = None):
         write_kaldi_matrix(out_fh, data, image_id)
 
 print('Generated features for {} images. Failed for {} (iamge too '
-      'long).'.format(num_ok, num_fail))
+      'long).'.format(num_ok, num_fail), file=sys.stderr)
diff --git a/egs/iam/v1/local/prepare_dict.sh b/egs/iam/v1/local/prepare_dict.sh
index 8b981de3abd..f691d577fba 100755
--- a/egs/iam/v1/local/prepare_dict.sh
+++ b/egs/iam/v1/local/prepare_dict.sh
@@ -8,6 +8,9 @@
 
 set -e
 dir=data/local/dict
+vocab_size=50000
+. ./utils/parse_options.sh
+
 mkdir -p $dir
 
 # First get the set of all letters that occur in data/train/text
@@ -22,7 +25,7 @@ cat data/train/text | \
 
 export letters=$(cat $dir/nonsilence_phones.txt | tr -d "\n")
 
-cat data/local/local_lm/data/wordlist | \
+head -n $vocab_size data/local/local_lm/data/word_count | awk '{print $2}' | \
   perl -e '$letters=$ENV{letters};
 while(<>){
     chop;
diff --git a/egs/iam/v1/local/train_lm.sh b/egs/iam/v1/local/train_lm.sh
index a673c5b3f2d..c47284e7692 100755
--- a/egs/iam/v1/local/train_lm.sh
+++ b/egs/iam/v1/local/train_lm.sh
@@ -108,7 +108,6 @@ if [ $stage -le 1 ]; then
                ${dir}/data/text ${order} ${lm_dir}/work ${unpruned_lm_dir}
 
   get_data_prob.py ${dir}/data/real_dev_set.txt ${unpruned_lm_dir} 2>&1 | grep -F '[perplexity'
-  #log-prob: -5.05603614242 [perplexity = 156.967086371] over 19477.0 words
 fi
 
 if [ $stage -le 2 ]; then
@@ -118,9 +117,6 @@ if [ $stage -le 2 ]; then
   prune_lm_dir.py --target-num-ngrams=$size --initial-threshold=0.02 ${unpruned_lm_dir} ${dir}/data/lm_${order}_prune_big
 
   get_data_prob.py ${dir}/data/real_dev_set.txt ${dir}/data/lm_${order}_prune_big 2>&1 | grep -F '[perplexity'
-  # get_data_prob.py: log-prob of data/local/local_lm/data/real_dev_set.txt given model data/local/local_lm/data/lm_3_prune_big was -5.06654404785 per word [perplexity = 158.625177948] over 19477.0 words
-  # current results, after adding --limit-unk-history=true:
-
 
   mkdir -p ${dir}/data/arpa
   format_arpa_lm.py ${dir}/data/lm_${order}_prune_big | gzip -c > ${dir}/data/arpa/${order}gram_big.arpa.gz
@@ -134,9 +130,6 @@ if [ $stage -le 3 ]; then
   prune_lm_dir.py --target-num-ngrams=$size ${dir}/data/lm_${order}_prune_big ${dir}/data/lm_${order}_prune_small
 
   get_data_prob.py ${dir}/data/real_dev_set.txt ${dir}/data/lm_${order}_prune_small 2>&1 | grep -F '[perplexity'
-  # get_data_prob.py: log-prob of data/local/local_lm/data/real_dev_set.txt given model data/local/local_lm/data/lm_3_prune_small was -5.24719139498 per word [perplexity = 190.031793995] over 19477.0 words
-  # current results, after adding --limit-unk-history=true (needed for modeling OOVs and not blowing up LG.fst):
-
 
   format_arpa_lm.py ${dir}/data/lm_${order}_prune_small | gzip -c > ${dir}/data/arpa/${order}gram_small.arpa.gz
 fi
diff --git a/egs/iam/v1/run.sh b/egs/iam/v1/run.sh
index f5c4a2b8f80..00c9f682bf2 100755
--- a/egs/iam/v1/run.sh
+++ b/egs/iam/v1/run.sh
@@ -44,22 +44,32 @@ if [ $stage -le 2 ]; then
   echo "$0: Estimating a language model for decoding..."
   # We do this stage before dict preparation because prepare_dict.sh
   # generates the lexicon from pocolm's wordlist
-  local/train_lm.sh --vocab-size 50000
+  local/train_lm.sh --vocab-size 50k
 fi
 
 if [ $stage -le 3 ]; then
   echo "$0: Preparing dictionary and lang..."
-  local/prepare_dict.sh
+
+  # This is for training. Use a large vocab size, e.g. 500k to include all the
+  # training words:
+  local/prepare_dict.sh --vocab-size 500k --dir data/local/dict  # this is for training
   utils/prepare_lang.sh --num-sil-states 4 --num-nonsil-states 8 --sil-prob 0.95 \
                         data/local/dict "<unk>" data/lang/temp data/lang
-  utils/format_lm.sh data/lang data/local/local_lm/data/arpa/3gram_big.arpa.gz \
-                     data/local/dict/lexicon.txt data/lang_test
+
+  # This is for decoding. We use a 50k lexicon to be consistent with the papers
+  # reporting WERs on IAM:
+  local/prepare_dict.sh --vocab-size 50k --dir data/local/dict_50k  # this is for decoding
+  utils/prepare_lang.sh --num-sil-states 4 --num-nonsil-states 8 --sil-prob 0.95 \
+                        data/local/dict_50k "<unk>" data/lang_test/temp data/lang_test
+  utils/format_lm.sh data/lang_test data/local/local_lm/data/arpa/3gram_big.arpa.gz \
+                     data/local/dict_50k/lexicon.txt data/lang_test
+
   echo "$0: Preparing the unk model for open-vocab decoding..."
   utils/lang/make_unk_lm.sh --ngram-order 4 --num-extra-ngrams 7500 \
-                            data/local/dict exp/unk_lang_model
+                            data/local/dict_50k exp/unk_lang_model
   utils/prepare_lang.sh --num-sil-states 4 --num-nonsil-states 8 \
                         --unk-fst exp/unk_lang_model/unk_fst.txt \
-                        data/local/dict "<unk>" data/local/temp data/lang_unk
+                        data/local/dict_50k "<unk>" data/lang_unk/temp data/lang_unk
   cp data/lang_test/G.fst data/lang_unk/G.fst
 fi
 
@@ -131,5 +141,5 @@ if [ $stage -le 13 ]; then
 fi
 
 if [ $stage -le 14 ]; then
-  local/chain/run_cnn_chainali_1b.sh --chain-model-dir exp/chain/cnn_1a --stage 2
+  local/chain/run_cnn_chainali_1c.sh --chain-model-dir exp/chain/cnn_1a --stage 2
 fi
diff --git a/egs/iam/v1/run_end2end.sh b/egs/iam/v1/run_end2end.sh
index d479bfa2a73..c9db5889d29 100755
--- a/egs/iam/v1/run_end2end.sh
+++ b/egs/iam/v1/run_end2end.sh
@@ -29,7 +29,7 @@ fi
 mkdir -p data/{train,test}/data
 
 if [ $stage -le 1 ]; then
-  get_image2num_frames.py data/train  # This will be needed for the next command
+  image/get_image2num_frames.py data/train  # This will be needed for the next command
   # The next command creates a "allowed_lengths.txt" file in data/train
   # which will be used by local/make_features.py to enforce the images to
   # have allowed lengths. The allowed lengths will be spaced by 10% difference in length.
@@ -45,32 +45,38 @@ if [ $stage -le 1 ]; then
 fi
 
 if [ $stage -le 2 ]; then
-  echo "$0: Preparing dictionary and lang..."
-  local/prepare_dict.sh
-  utils/prepare_lang.sh --sil-prob 0.95 \
-                        data/local/dict "<unk>" data/lang/temp data/lang
+  echo "$0: Estimating a language model for decoding..."
+  # We do this stage before dict preparation because prepare_dict.sh
+  # generates the lexicon from pocolm's wordlist
+  local/train_lm.sh --vocab-size 50k
 fi
 
 if [ $stage -le 3 ]; then
-  echo "$0: Estimating a language model for decoding..."
-  local/train_lm.sh
-  utils/format_lm.sh data/lang data/local/local_lm/data/arpa/3gram_big.arpa.gz \
-                     data/local/dict/lexicon.txt data/lang_test
-fi
+  echo "$0: Preparing dictionary and lang..."
+
+  # This is for training. Use a large vocab size, e.g. 500k to include all the
+  # training words:
+  local/prepare_dict.sh --vocab-size 500k --dir data/local/dict
+  utils/prepare_lang.sh --sil-prob 0.95 \
+                        data/local/dict "<unk>" data/lang/temp data/lang
 
+  # This is for decoding. We use a 50k lexicon to be consistent with the papers
+  # reporting WERs on IAM.
+  local/prepare_dict.sh --vocab-size 50k --dir data/local/dict_50k
+  utils/prepare_lang.sh --sil-prob 0.95 data/local/dict_50k \
+                        "<unk>" data/lang_test/temp data/lang_test
+  utils/format_lm.sh data/lang_test data/local/local_lm/data/arpa/3gram_big.arpa.gz \
+                     data/local/dict_50k/lexicon.txt data/lang_test
 
-if [ $stage -le 4 ]; then
-  echo "$0: estimating phone language model for the denominator graph"
-  mkdir -p exp/chain/e2e_base/log
-  $cmd exp/chain/e2e_base/log/make_phone_lm.log \
-  cat data/train/text \| \
-    steps/nnet3/chain/e2e/text_to_phones.py data/lang \| \
-    utils/sym2int.pl -f 2- data/lang/phones.txt \| \
-    chain-est-phone-lm --num-extra-lm-states=1000 \
-                       ark:- exp/chain/e2e_base/phone_lm.fst
+  echo "$0: Preparing the unk model for open-vocab decoding..."
+  utils/lang/make_unk_lm.sh --ngram-order 4 --num-extra-ngrams 7500 \
+                            data/local/dict_50k exp/unk_lang_model
+  utils/prepare_lang.sh --unk-fst exp/unk_lang_model/unk_fst.txt \
+                        data/local/dict_50k "<unk>" data/lang_unk/temp data/lang_unk
+  cp data/lang_test/G.fst data/lang_unk/G.fst
 fi
 
-if [ $stage -le 5 ]; then
-  echo "$0: calling the flat-start chain recipe..."
+if [ $stage -le 4 ]; then
+  echo "$0: Calling the flat-start chain recipe..."
   local/chain/run_flatstart_cnn1a.sh
 fi