============================================================================

                     MonoPhone Training & Decoding

============================================================================

steps/train_mono.sh --nj 30 --cmd run.pl --mem 4G data/train data/lang exp/mono

steps/train_mono.sh: Initializing monophone system.

steps/train_mono.sh: Compiling training graphs

steps/train_mono.sh: Aligning data equally (pass 0)

steps/train_mono.sh: Pass 1

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 2

steps/train_mono.sh: Aligning data
....

steps/diagnostic/analyze_alignments.sh --cmd run.pl --mem 4G data/lang exp/mono
steps/diagnostic/analyze_alignments.sh: see stats in exp/mono/log/analyze_alignments.log
2 warnings in exp/mono/log/align.*.*.log
exp/mono: nj=30 align prob=-99.15 over 3.12h [retry=0.0%, fail=0.0%] states=144 gauss=986
steps/train_mono.sh: Done training monophone system in exp/mono

tree-info exp/mono/tree

tree-info exp/mono/tree

fsttablecompose data/lang_test_bg/L_disambig.fst data/lang_test_bg/G.fst

fstdeterminizestar --use-log=true

fstminimizeencoded

fstpushspecial

fstisstochastic data/lang_test_bg/tmp/LG.fst

-0.00841336 -0.00928521

fstcomposecontext --context-size=1 --central-position=0 --read-disambig-syms=data/lang_test_bg/phones/disambig.int --write-disambig-syms=data/lang_test_bg/tmp/disambig_ilabels_1_0.int data/lang_test_bg/tmp/ilabels_1_0.31072

fstisstochastic data/lang_test_bg/tmp/CLG_1_0.fst

-0.00841336 -0.00928521

make-h-transducer --disambig-syms-out=exp/mono/graph/disambig_tid.int --transition-scale=1.0 data/lang_test_bg/tmp/ilabels_1_0 exp/mono/tree exp/mono/final.mdl

fstminimizeencoded

fstdeterminizestar --use-log=true

fsttablecompose exp/mono/graph/Ha.fst data/lang_test_bg/tmp/CLG_1_0.fst

fstrmsymbols exp/mono/graph/disambig_tid.int

fstrmepslocal

fstisstochastic exp/mono/graph/HCLGa.fst

0.000381709 -0.00951555

add-self-loops --self-loop-scale=0.1 --reorder=true exp/mono/final.mdl

steps/decode.sh --nj 5 --cmd run.pl --mem 4G exp/mono/graph data/dev exp/mono/decode_dev

decode.sh: feature type is delta

steps/diagnostic/analyze_lats.sh --cmd run.pl --mem 4G exp/mono/graph exp/mono/decode_dev

steps/diagnostic/analyze_lats.sh: see stats in exp/mono/decode_dev/log/analyze_alignments.log

Overall, lattice depth (10,50,90-percentile)=(5,25,121) and mean=56.0

steps/diagnostic/analyze_lats.sh: see stats in exp/mono/decode_dev/log/analyze_lattice_depth_stats.log

steps/decode.sh --nj 5 --cmd run.pl --mem 4G exp/mono/graph data/test exp/mono/decode_test

decode.sh: feature type is delta

steps/diagnostic/analyze_lats.sh --cmd run.pl --mem 4G exp/mono/graph exp/mono/decode_test

steps/diagnostic/analyze_lats.sh: see stats in exp/mono/decode_test/log/analyze_alignments.log

Overall, lattice depth (10,50,90-percentile)=(6,27,143) and mean=70.8

steps/diagnostic/analyze_lats.sh: see stats in exp/mono/decode_test/log/analyze_lattice_depth_stats.log
============================================================================

           tri1 : Deltas + Delta-Deltas Training & Decoding

============================================================================

steps/align_si.sh --boost-silence 1.25 --nj 30 --cmd run.pl --mem 4G data/train data/lang exp/mono exp/mono_ali

steps/align_si.sh: feature type is delta

steps/align_si.sh: aligning data in data/train using model from exp/mono, putting alignments in exp/mono_ali

steps/diagnostic/analyze_alignments.sh --cmd run.pl --mem 4G data/lang exp/mono_ali

steps/diagnostic/analyze_alignments.sh: see stats in exp/mono_ali/log/analyze_alignments.log

steps/align_si.sh: done aligning data.

steps/train_deltas.sh --cmd run.pl --mem 4G 2500 15000 data/train data/lang exp/mono_ali exp/tri1

steps/train_deltas.sh: accumulating tree stats

steps/train_deltas.sh: getting questions for tree-building, via clustering

steps/train_deltas.sh: building the tree

steps/train_deltas.sh: converting alignments from exp/mono_ali to use current tree

steps/train_deltas.sh: compiling graphs of transcripts

steps/train_deltas.sh: training pass 1

steps/train_deltas.sh: training pass 2
...

steps/diagnostic/analyze_alignments.sh --cmd run.pl --mem 4G data/lang exp/tri1
steps/diagnostic/analyze_alignments.sh: see stats in exp/tri1/log/analyze_alignments.log

exp/tri1: nj=30 align prob=-95.28 over 3.12h [retry=0.0%, fail=0.0%] states=1893 gauss=15025 tree-impr=5.40
steps/train_deltas.sh: Done training system with delta+delta-delta features in exp/tri1

steps/train_deltas.sh: Done training system with delta+delta-delta features in exp/tri1
tree-info exp/tri1/tree
tree-info exp/tri1/tree
fstcomposecontext --context-size=3 --central-position=1 --read-disambig-syms=data/lang_test_bg/phones/disambig.int --write-disambig-syms=data/lang_test_bg/tmp/disambig_ilabels_3_1.int data/lang_test_bg/tmp/ilabels_3_1.28346
fstisstochastic data/lang_test_bg/tmp/CLG_3_1.fst
0 -0.00928518
make-h-transducer --disambig-syms-out=exp/tri1/graph/disambig_tid.int --transition-scale=1.0 data/lang_test_bg/tmp/ilabels_3_1 exp/tri1/tree exp/tri1/final.mdl
fsttablecompose exp/tri1/graph/Ha.fst data/lang_test_bg/tmp/CLG_3_1.fst
fstdeterminizestar --use-log=true
fstrmsymbols exp/tri1/graph/disambig_tid.int
fstminimizeencoded
fstrmepslocal
fstisstochastic exp/tri1/graph/HCLGa.fst
0.000449687 -0.0175771
HCLGa is not stochastic
add-self-loops --self-loop-scale=0.1 --reorder=true exp/tri1/final.mdl
steps/decode.sh --nj 5 --cmd run.pl --mem 4G exp/tri1/graph data/dev exp/tri1/decode_dev
decode.sh: feature type is delta
steps/diagnostic/analyze_lats.sh --cmd run.pl --mem 4G exp/tri1/graph exp/tri1/decode_dev
steps/diagnostic/analyze_lats.sh: see stats in exp/tri1/decode_dev/log/analyze_alignments.log
Overall, lattice depth (10,50,90-percentile)=(3,11,41) and mean=19.0
steps/diagnostic/analyze_lats.sh: see stats in exp/tri1/decode_dev/log/analyze_lattice_depth_stats.log
steps/decode.sh --nj 5 --cmd run.pl --mem 4G exp/tri1/graph data/test exp/tri1/decode_test
decode.sh: feature type is delta
steps/diagnostic/analyze_lats.sh --cmd run.pl --mem 4G exp/tri1/graph exp/tri1/decode_test
steps/diagnostic/analyze_lats.sh: see stats in exp/tri1/decode_test/log/analyze_alignments.log
Overall, lattice depth (10,50,90-percentile)=(3,12,47) and mean=21.8
steps/diagnostic/analyze_lats.sh: see stats in exp/tri1/decode_test/log/analyze_lattice_depth_stats.log

============================================================================

                 tri2 : LDA + MLLT Training & Decoding

============================================================================

steps/align_si.sh --nj 30 --cmd run.pl --mem 4G data/train data/lang exp/tri1 exp/tri1_ali

steps/align_si.sh: feature type is delta

steps/align_si.sh: aligning data in data/train using model from exp/tri1, putting alignments in exp/tri1_ali

steps/diagnostic/analyze_alignments.sh --cmd run.pl --mem 4G data/lang exp/tri1_ali

steps/diagnostic/analyze_alignments.sh: see stats in exp/tri1_ali/log/analyze_alignments.log

steps/align_si.sh: done aligning data.

steps/train_lda_mllt.sh --cmd run.pl --mem 4G --splice-opts --left-context=3 --right-context=3 2500 15000 data/train data/lang exp/tri1_ali exp/tri2

steps/train_lda_mllt.sh: Accumulating LDA statistics.

steps/train_lda_mllt.sh: Accumulating tree stats

steps/train_lda_mllt.sh: Getting questions for tree clustering.

steps/train_lda_mllt.sh: Building the tree

steps/train_lda_mllt.sh: Initializing the model

steps/train_lda_mllt.sh: Converting alignments from exp/tri1_ali to use current tree

steps/train_lda_mllt.sh: Compiling graphs of transcripts

Training pass 1

Training pass 2

steps/train_lda_mllt.sh: Estimating MLLT

Training pass 3

Training pass 4
...

steps/diagnostic/analyze_alignments.sh --cmd run.pl --mem 4G data/lang exp/tri2
steps/diagnostic/analyze_alignments.sh: see stats in exp/tri2/log/analyze_alignments.log
1 warnings in exp/tri2/log/compile_questions.log
110 warnings in exp/tri2/log/update.*.log
99 warnings in exp/tri2/log/init_model.log
exp/tri2: nj=30 align prob=-47.93 over 3.12h [retry=0.0%, fail=0.0%] states=2021 gauss=15026 tree-impr=5.57 lda-sum=28.43 mllt:impr,logdet=1.66,2.28
steps/train_lda_mllt.sh: Done training system with LDA+MLLT features in exp/tri2
tree-info exp/tri2/tree
tree-info exp/tri2/tree
make-h-transducer --disambig-syms-out=exp/tri2/graph/disambig_tid.int --transition-scale=1.0 data/lang_test_bg/tmp/ilabels_3_1 exp/tri2/tree exp/tri2/final.mdl
fsttablecompose exp/tri2/graph/Ha.fst data/lang_test_bg/tmp/CLG_3_1.fst
fstminimizeencoded
fstdeterminizestar --use-log=true
fstrmsymbols exp/tri2/graph/disambig_tid.int
fstrmepslocal
fstisstochastic exp/tri2/graph/HCLGa.fst
0.000472258 -0.0175772
HCLGa is not stochastic
add-self-loops --self-loop-scale=0.1 --reorder=true exp/tri2/final.mdl
steps/decode.sh --nj 5 --cmd run.pl --mem 4G exp/tri2/graph data/dev exp/tri2/decode_dev
decode.sh: feature type is lda
steps/diagnostic/analyze_lats.sh --cmd run.pl --mem 4G exp/tri2/graph exp/tri2/decode_dev
steps/diagnostic/analyze_lats.sh: see stats in exp/tri2/decode_dev/log/analyze_alignments.log
Overall, lattice depth (10,50,90-percentile)=(2,8,29) and mean=13.2
steps/diagnostic/analyze_lats.sh: see stats in exp/tri2/decode_dev/log/analyze_lattice_depth_stats.log
steps/decode.sh --nj 5 --cmd run.pl --mem 4G exp/tri2/graph data/test exp/tri2/decode_test
decode.sh: feature type is lda
steps/diagnostic/analyze_lats.sh --cmd run.pl --mem 4G exp/tri2/graph exp/tri2/decode_test
steps/diagnostic/analyze_lats.sh: see stats in exp/tri2/decode_test/log/analyze_alignments.log
Overall, lattice depth (10,50,90-percentile)=(2,9,33) and mean=14.9
steps/diagnostic/analyze_lats.sh: see stats in exp/tri2/decode_test/log/analyze_lattice_depth_stats.log

============================================================================

              tri3 : LDA + MLLT + SAT Training & Decoding

============================================================================

steps/align_si.sh --nj 30 --cmd run.pl --mem 4G --use-graphs true data/train data/lang exp/tri2 exp/tri2_ali

steps/align_si.sh: feature type is lda

steps/align_si.sh: aligning data in data/train using model from exp/tri2, putting alignments in exp/tri2_ali

steps/diagnostic/analyze_alignments.sh --cmd run.pl --mem 4G data/lang exp/tri2_ali

steps/diagnostic/analyze_alignments.sh: see stats in exp/tri2_ali/log/analyze_alignments.log

steps/align_si.sh: done aligning data.

steps/train_sat.sh --cmd run.pl --mem 4G 2500 15000 data/train data/lang exp/tri2_ali exp/tri3

steps/train_sat.sh: feature type is lda

steps/train_sat.sh: obtaining initial fMLLR transforms since not present in exp/tri2_ali

steps/train_sat.sh: Accumulating tree stats

steps/train_sat.sh: Getting questions for tree clustering.

steps/train_sat.sh: Building the tree

steps/train_sat.sh: Initializing the model

steps/train_sat.sh: Converting alignments from exp/tri2_ali to use current tree

steps/train_sat.sh: Compiling graphs of transcripts

Pass 1

Pass 2

Estimating fMLLR transforms

Pass 3

Pass 4
...

steps/diagnostic/analyze_alignments.sh --cmd run.pl --mem 4G data/lang exp/tri3
steps/diagnostic/analyze_alignments.sh: see stats in exp/tri3/log/analyze_alignments.log
15 warnings in exp/tri3/log/update.*.log
43 warnings in exp/tri3/log/init_model.log
1 warnings in exp/tri3/log/compile_questions.log
steps/train_sat.sh: Likelihood evolution:
-50.2406 -49.3636 -49.1648 -48.9681 -48.2487 -47.5314 -47.0963 -46.8406 -46.6005 -46.0718 -45.8132 -45.4844 -45.2978 -45.1566 -45.0342 -44.9283 -44.8193 -44.7107 -44.6095 -44.4491 -44.3126 -44.2252 -44.1415 -44.0622 -43.9841 -43.9087 -43.8345 -43.7629 -43.6936 -43.6003 -43.5265 -43.5005 -43.484 -43.4717
exp/tri3: nj=30 align prob=-47.09 over 3.12h [retry=0.0%, fail=0.0%] states=1920 gauss=15011 fmllr-impr=4.04 over 2.79h tree-impr=8.82
steps/train_sat.sh: done training SAT system in exp/tri3
tree-info exp/tri3/tree
tree-info exp/tri3/tree
make-h-transducer --disambig-syms-out=exp/tri3/graph/disambig_tid.int --transition-scale=1.0 data/lang_test_bg/tmp/ilabels_3_1 exp/tri3/tree exp/tri3/final.mdl
fsttablecompose exp/tri3/graph/Ha.fst data/lang_test_bg/tmp/CLG_3_1.fst
fstrmepslocal
fstdeterminizestar --use-log=true
fstrmsymbols exp/tri3/graph/disambig_tid.int
fstminimizeencoded
fstisstochastic exp/tri3/graph/HCLGa.fst
0.000444886 -0.0175772
HCLGa is not stochastic
add-self-loops --self-loop-scale=0.1 --reorder=true exp/tri3/final.mdl
steps/decode_fmllr.sh --nj 5 --cmd run.pl --mem 4G exp/tri3/graph data/dev exp/tri3/decode_dev
steps/decode.sh --scoring-opts --num-threads 1 --skip-scoring false --acwt 0.083333 --nj 5 --cmd run.pl --mem 4G --beam 10.0 --model exp/tri3/final.alimdl --max-active 2000 exp/tri3/graph data/dev exp/tri3/decode_dev.si
decode.sh: feature type is lda
steps/diagnostic/analyze_lats.sh --cmd run.pl --mem 4G exp/tri3/graph exp/tri3/decode_dev.si
steps/diagnostic/analyze_lats.sh: see stats in exp/tri3/decode_dev.si/log/analyze_alignments.log
Overall, lattice depth (10,50,90-percentile)=(2,9,34) and mean=15.6

steps/diagnostic/analyze_lats.sh: see stats in exp/tri3/decode_dev.si/log/analyze_lattice_depth_stats.log
steps/decode_fmllr.sh: feature type is lda
steps/decode_fmllr.sh: getting first-pass fMLLR transforms.
steps/decode_fmllr.sh: doing main lattice generation phase
steps/decode_fmllr.sh: estimating fMLLR transforms a second time.
steps/decode_fmllr.sh: doing a final pass of acoustic rescoring.
steps/diagnostic/analyze_lats.sh --cmd run.pl --mem 4G exp/tri3/graph exp/tri3/decode_dev
steps/diagnostic/analyze_lats.sh: see stats in exp/tri3/decode_dev/log/analyze_alignments.log
Overall, lattice depth (10,50,90-percentile)=(1,5,16) and mean=7.7
steps/diagnostic/analyze_lats.sh: see stats in exp/tri3/decode_dev/log/analyze_lattice_depth_stats.log
steps/decode_fmllr.sh --nj 5 --cmd run.pl --mem 4G exp/tri3/graph data/test exp/tri3/decode_test
steps/decode.sh --scoring-opts --num-threads 1 --skip-scoring false --acwt 0.083333 --nj 5 --cmd run.pl --mem 4G --beam 10.0 --model exp/tri3/final.alimdl --max-active 2000 exp/tri3/graph data/test exp/tri3/decode_test.si
decode.sh: feature type is lda
steps/diagnostic/analyze_lats.sh --cmd run.pl --mem 4G exp/tri3/graph exp/tri3/decode_test.si
steps/diagnostic/analyze_lats.sh: see stats in exp/tri3/decode_test.si/log/analyze_alignments.log
Overall, lattice depth (10,50,90-percentile)=(2,10,37) and mean=16.8
steps/diagnostic/analyze_lats.sh: see stats in exp/tri3/decode_test.si/log/analyze_lattice_depth_stats.log
steps/decode_fmllr.sh: feature type is lda
steps/decode_fmllr.sh: getting first-pass fMLLR transforms.
steps/decode_fmllr.sh: doing main lattice generation phase
steps/decode_fmllr.sh: estimating fMLLR transforms a second time.
steps/decode_fmllr.sh: doing a final pass of acoustic rescoring.
steps/diagnostic/analyze_lats.sh --cmd run.pl --mem 4G exp/tri3/graph exp/tri3/decode_test
steps/diagnostic/analyze_lats.sh: see stats in exp/tri3/decode_test/log/analyze_alignments.log
Overall, lattice depth (10,50,90-percentile)=(1,5,18) and mean=8.6
steps/diagnostic/analyze_lats.sh: see stats in exp/tri3/decode_test/log/analyze_lattice_depth_stats.log

============================================================================

                        SGMM2 Training & Decoding

============================================================================

steps/align_fmllr.sh --nj 30 --cmd run.pl --mem 4G data/train data/lang exp/tri3 exp/tri3_ali

steps/align_fmllr.sh: feature type is lda

steps/align_fmllr.sh: compiling training graphs

steps/align_fmllr.sh: aligning data in data/train using exp/tri3/final.alimdl and speaker-independent features.

steps/align_fmllr.sh: computing fMLLR transforms

steps/align_fmllr.sh: doing final alignment.

steps/align_fmllr.sh: done aligning data.

steps/diagnostic/analyze_alignments.sh --cmd run.pl --mem 4G data/lang exp/tri3_ali

steps/diagnostic/analyze_alignments.sh: see stats in exp/tri3_ali/log/analyze_alignments.log

steps/train_ubm.sh --cmd run.pl --mem 4G 400 data/train data/lang exp/tri3_ali exp/ubm4
steps/train_ubm.sh: feature type is lda
steps/train_ubm.sh: using transforms from exp/tri3_ali
steps/train_ubm.sh: clustering model exp/tri3_ali/final.mdl to get initial UBM
steps/train_ubm.sh: doing Gaussian selection
Pass 0
Pass 1
Pass 2
steps/train_sgmm2.sh --cmd run.pl --mem 4G 7000 9000 data/train data/lang exp/tri3_ali exp/ubm4/final.ubm exp/sgmm2_4
steps/train_sgmm2.sh: feature type is lda
steps/train_sgmm2.sh: using transforms from exp/tri3_ali
steps/train_sgmm2.sh: accumulating tree stats
steps/train_sgmm2.sh: Getting questions for tree clustering.
steps/train_sgmm2.sh: Building the tree
steps/train_sgmm2.sh: Initializing the model
steps/train_sgmm2.sh: doing Gaussian selection
steps/train_sgmm2.sh: compiling training graphs
steps/train_sgmm2.sh: converting alignments
steps/train_sgmm2.sh: training pass 0 ...
steps/train_sgmm2.sh: training pass 1 ...
steps/train_sgmm2.sh: training pass 2 ...
steps/train_sgmm2.sh: training pass 3 ...
steps/train_sgmm2.sh: training pass 4 ...
steps/train_sgmm2.sh: training pass 5 ...
steps/train_sgmm2.sh: re-aligning data
steps/train_sgmm2.sh: training pass 6 ...
steps/train_sgmm2.sh: training pass 7 ...
steps/train_sgmm2.sh: training pass 8 ...
steps/train_sgmm2.sh: training pass 9 ...
steps/train_sgmm2.sh: training pass 10 ...
steps/train_sgmm2.sh: re-aligning data
steps/train_sgmm2.sh: training pass 11 ...
steps/train_sgmm2.sh: training pass 12 ...
steps/train_sgmm2.sh: training pass 13 ...
steps/train_sgmm2.sh: training pass 14 ...
steps/train_sgmm2.sh: training pass 15 ...
steps/train_sgmm2.sh: re-aligning data
steps/train_sgmm2.sh: training pass 16 ...
steps/train_sgmm2.sh: training pass 17 ...
steps/train_sgmm2.sh: training pass 18 ...
steps/train_sgmm2.sh: training pass 19 ...
steps/train_sgmm2.sh: training pass 20 ...
steps/train_sgmm2.sh: training pass 21 ...
steps/train_sgmm2.sh: training pass 22 ...
steps/train_sgmm2.sh: training pass 23 ...
steps/train_sgmm2.sh: training pass 24 ...
steps/train_sgmm2.sh: building alignment model (pass 25)
steps/train_sgmm2.sh: building alignment model (pass 26)
steps/train_sgmm2.sh: building alignment model (pass 27)
198 warnings in exp/sgmm2_4/log/update_ali.*.log
1723 warnings in exp/sgmm2_4/log/update.*.log
1 warnings in exp/sgmm2_4/log/compile_questions.log
Done
tree-info exp/sgmm2_4/tree
tree-info exp/sgmm2_4/tree
make-h-transducer --disambig-syms-out=exp/sgmm2_4/graph/disambig_tid.int --transition-scale=1.0 data/lang_test_bg/tmp/ilabels_3_1 exp/sgmm2_4/tree exp/sgmm2_4/final.mdl
fsttablecompose exp/sgmm2_4/graph/Ha.fst data/lang_test_bg/tmp/CLG_3_1.fst
fstrmsymbols exp/sgmm2_4/graph/disambig_tid.int
fstrmepslocal
fstdeterminizestar --use-log=true
fstminimizeencoded
fstisstochastic exp/sgmm2_4/graph/HCLGa.fst
0.000485195 -0.0175772
HCLGa is not stochastic
add-self-loops --self-loop-scale=0.1 --reorder=true exp/sgmm2_4/final.mdl
steps/decode_sgmm2.sh --nj 5 --cmd run.pl --mem 4G --transform-dir exp/tri3/decode_dev exp/sgmm2_4/graph data/dev exp/sgmm2_4/decode_dev
steps/decode_sgmm2.sh: feature type is lda
steps/decode_sgmm2.sh: using transforms from exp/tri3/decode_dev
steps/diagnostic/analyze_lats.sh --cmd run.pl --mem 4G exp/sgmm2_4/graph exp/sgmm2_4/decode_dev
steps/diagnostic/analyze_lats.sh: see stats in exp/sgmm2_4/decode_dev/log/analyze_alignments.log
Overall, lattice depth (10,50,90-percentile)=(2,6,20) and mean=9.5
steps/diagnostic/analyze_lats.sh: see stats in exp/sgmm2_4/decode_dev/log/analyze_lattice_depth_stats.log
steps/decode_sgmm2.sh --nj 5 --cmd run.pl --mem 4G --transform-dir exp/tri3/decode_test exp/sgmm2_4/graph data/test exp/sgmm2_4/decode_test
steps/decode_sgmm2.sh: feature type is lda
steps/decode_sgmm2.sh: using transforms from exp/tri3/decode_test
steps/diagnostic/analyze_lats.sh --cmd run.pl --mem 4G exp/sgmm2_4/graph exp/sgmm2_4/decode_test
steps/diagnostic/analyze_lats.sh: see stats in exp/sgmm2_4/decode_test/log/analyze_alignments.log
Overall, lattice depth (10,50,90-percentile)=(2,7,24) and mean=11.0
steps/diagnostic/analyze_lats.sh: see stats in exp/sgmm2_4/decode_test/log/analyze_lattice_depth_stats.log

kaldi的TIMIT实例二的更多相关文章

  1. kaldi的TIMIT实例三

    ============================================================================ MMI + SGMM2 Training &a ...

  2. kaldi的TIMIT实例一

    TIMIT语音库是IT和MIT合作音素级别标注的语音库,用于自动语音识别系统的发展和评估,包括来自美式英语,8个地区方言,630个人. 每个人读10个句子,每个发音都是音素级别.词级别文本标注,16k ...

  3. C语言库函数大全及应用实例二

    原文:C语言库函数大全及应用实例二                                              [编程资料]C语言库函数大全及应用实例二 函数名: bioskey 功 能 ...

  4. Hibernate实例二

    Hibernate实例二 一.测试openSession方法和getCurrentSession方法 hebernate中可以通过上述两种方法获取session对象以对数据库进行操作,下面的代码以及注 ...

  5. Ajax实例二：取得新内容

    Ajax实例二:取得新内容 通过点击pre和next按钮,从服务器取得最新内容. HTML代码 <div id="slide">图片显示区</div> &l ...

  6. WPF中的多进程(Threading)处理实例(二)

    原文:WPF中的多进程(Threading)处理实例(二) //错误的处理 private void cmdBreakRules_Click(object sender, RoutedEventArg ...

  7. express+nodecoffee写passport登录验证实例(二）

    二:实现登录认证 passport官网文档:  http://passportjs.org/guide/ passport验证使用一种被称为“策略”的方式来验证请求,策略支持3种类型的验证:用户名密码 ...

  8. DWR入门实例(二)

    DWR(Direct Web Remoting) DWR is a Java library that enables Java on the server and JavaScript in a b ...

  9. [原创]MongoDB综合实例二

    MongoDB-Sharding部署方案 一.    部署环境 五台主机: Amongoshard01:  10.212.74.43 Amongoshard02:  10.212.84.4 Among ...

随机推荐

  1. C程序设计第一次实验报告

    C程序设计实验报告 实验项目: 1.字符与ASCII码 2.运算符与表达式的运用 3.顺序结构应用程序 4.数学函数的算法描述 5.鸡兔同笼的算法描述 6.确定坐标的运算描述 姓名:王煜 实验地点:教 ...

  2. 富文本编辑器summerNote

    载入富文本: $('.summernote').summernote({ height: 220, tabsize: 2, lang: 'zh-CN' }); 富文本获取内容: $('.summern ...

  3. MFC---关于string.h相关函数

    1.在VS2005中使用strcpy.strcat.sprintf出现如:mfc中'strcpy' was declared deprecated警告 这是因为VS2005中认为CRT中的一组函数如果 ...

  4. keepalive高可用

    Keepalived软件起初是专门为LVS负载均衡软件设计的,用来管理并监控LVS集群系统中各个服务节点的状态,后来又加入了可以实现高可用的VRRP功能.因此,Keepalived除了能够管理LVS软 ...

  5. combox省市县三级联动

    /** * Name 获取省份(初始化) */ function showProvince(id1, id2, id3) { var paramData = {}; $.ajax({ url: osp ...

  6. Jenkin配置执行远程shell命令

    转载自 http://www.cnblogs.com/parryyang/p/6261730.html 在利用jenkins进行集成化部署的时候,我们在部署生成的war包时,往往需要进行一些备份,或者 ...

  7. 大数据学习笔记1-大数据处理架构Hadoop

    Hadoop:一个开源的.可运行于大规模集群上的分布式计算平台.实现了MapReduce计算模型和分布式文件系统HDFS等功能,方便用户轻松编写分布式并行程序. Hadoop生态系统: HDFS:Ha ...

  8. IBGP规划

    在IBGP邻居中,使用反射器减少IBGP邻居关系是一种最常用的网络结构,在上图中AS65001中有6台路由器,其中,R1和R2是一组反射器,配置如下: R1: ! interface Loopback ...

  9. paired-end reads的拼接

    paired-end reads的拼接 发表于2012 年 8 月 13 日 Velvet中paired-end reads的拼接 文件格式 要将两头测序(paired-end)的reads放到同一个 ...

  10. 选择困难症的福音——团队Scrum冲刺阶段-Day 7

    选择困难症的福音--团队Scrum冲刺阶段-Day 7 今日进展 测试代码 将界面设计完后放app使用示意图于此 今日贡献量 严域俊 吴恒佚 曾程 刘辰 邓煜坤 3.5 3.5 3.3 3.6 3 贡 ...