Updated RESULTS file.

git-svn-id: https://svn.code.sf.net/p/kaldi/code/trunk@95 5e6a8d80-dfce-4ca6-a32a-6e07a63d50c8

egs/rm/s1/RESULTS

@@ -40,13 +40,20 @@ exp/decode_tri2l_utt/wer:Average WER is 5.066624 (635 / 12533)  # [ as decode_tr
 
 # sgmma is SGMM without speaker vectors.
 exp/decode_sgmma/wer:Average WER is 3.151680 (395 / 12533) 
-exp/decode_sgmma_fmllr/wer:Average WER is 2.768691 (347 / 12533) 
+exp/decode_sgmma_fmllr/wer:Average WER is 2.728796 (342 / 12533) 
+exp/decode_sgmma_fmllr_utt/wer:Average WER is 3.087848 (387 / 12533) 
+exp/decode_sgmma_fmllrbasis_utt/wer:Average WER is 2.896354 (363 / 12533) 
 
 # sgmmb is SGMM with speaker vectors.
 exp/decode_sgmmb/wer:Average WER is 2.617091 (328 / 12533) 
 exp/decode_sgmmb_utt/wer:Average WER is 2.696880 (338 / 12533) 
 exp/decode_sgmmb_fmllr/wer:Average WER is 2.505386 (314 / 12533) 
 
+# note: when changing (phn,spk) dimensions from (40,39) -> (30,30),
+# WER in decode_sgmmb/ went from 2.62 to 2.92
+# when changing from (40,39) -> (50,39)  [40->50 on iter 3],
+# WER in decode_sgmmb/ went from 2.62 to 2.66 [and test likelihood
+# got worse].
 
 # sgmmc is as sgmmb but with gender-dependent UBM, with 250
 # Gaussians per gender instead of 400 Gaussians.  Note: use

egs/rm/s1/path.sh

@@ -1 +1,2 @@
-export PATH=$PATH:../../../src/bin:../../../tools/openfst/bin:../../../src/fstbin/:../../../src/gmmbin/:../../../src/featbin/:../../../src/fgmmbin:../../../src/sgmmbin
+root=../../..
+export PATH=${root}/src/bin:${root}/tools/openfst/bin:${root}/src/fstbin/:${root}/src/gmmbin/:${root}/src/featbin/:${root}/src/fgmmbin:${root}/src/sgmmbin:$PATH

egs/rm/s1/run.sh

@@ -107,7 +107,9 @@ steps/train_ubma.sh
 # You can take this all the way down to 1 for fastest speed, although
 # this will degrade results.
 
-(steps/train_sgmma.sh; steps/decode_sgmma.sh)&
+(steps/train_sgmma.sh; steps/decode_sgmma.sh; steps/decode_sgmma_fmllr.sh;
+ steps/decode_sgmma_fmllr_utt.sh; steps/train_sgmma_fmllrbasis.sh; 
+ steps/decode_sgmma_fmllrbasis_utt.sh )&
 
 # train and test system with speaker vectors.
 (steps/train_sgmmb.sh; steps/decode_sgmmb.sh; steps/decode_sgmmb_fmllr.sh; steps/decode_sgmmb_utt.sh )&

egs/rm/s1/steps/train_sgmma.sh

@@ -37,7 +37,7 @@ totsubstates=7500 # Target #-substates.
 maxiterinc=15 # Last iter to increase #substates on.
 
 incsubstates=$[($totsubstates-$numsubstates)/$maxiterinc] # per-iter increment for #substates
-gselect_opt="--gselect=ark:gunzip -c $dir/gselect.gz|"
+gselect_opt="--gselect=ark,s,cs:gunzip -c $dir/gselect.gz|"
 randprune=0.1
 mkdir -p $dir
 

egs/rm/s1/steps/train_sgmmb.sh

@@ -38,7 +38,7 @@ numsubstates=2500 # Initial #-substates.
 totsubstates=7500 # Target #-substates.
 maxiterinc=15 # Last iter to increase #substates on.
 incsubstates=$[($totsubstates-$numsubstates)/$maxiterinc] # per-iter increment for #substates
-gselect_opt="--gselect=ark:gunzip -c $dir/gselect.gz|"
+gselect_opt="--gselect=ark,s,cs:gunzip -c $dir/gselect.gz|"
 # Initially don't have speaker vectors, but change this after
 # we estimate them.
 spkvecs_opt=

egs/rm/s1/steps/train_sgmmc.sh

@@ -39,7 +39,7 @@ numsubstates=2500 # Initial #-substates.
 totsubstates=7500 # Target #-substates.
 maxiterinc=15 # Last iter to increase #substates on.
 incsubstates=$[($totsubstates-$numsubstates)/$maxiterinc] # per-iter increment for #substates
-gselect_opt="--gselect=ark:gunzip -c $dir/gselect.gz|"
+gselect_opt="--gselect=ark,s,cs:gunzip -c $dir/gselect.gz|"
 # Initially don't have speaker vectors, but change this after
 # we estimate them.
 spkvecs_opt=
@@ -98,7 +98,7 @@ if [ ! -f $dir/0.mdl ]; then
 fi
 
 if [ ! -f $dir/gselect.gz ]; then
-   sgmm-gselect "--preselect=ark:gunzip -c $dir/preselect.gz|" \
+   sgmm-gselect "--preselect=ark,s,cs:gunzip -c $dir/preselect.gz|" \
      $dir/0.mdl "$feats" ark,t:- 2>$dir/gselect.log | gzip -c > $dir/gselect.gz || exit 1;
 fi