“2013-04-19”版本间的差异

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1. Data sharing
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==Data sharing==
  
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(1) AM/lexicon/LM are shared.
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* AM/lexicon/LM are shared.
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* LM count files are still in transfering.  
  
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(2) LM count files are still in transfering.  
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==DNN progress==
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===400 hour DNN training===
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{| class="wikitable"
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!Test Set!! Tencent Baseline!! bMMI!! fMMI !! BN(with fMMI) !! Hybrid
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|-
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|1900||8.4  || 7.65 || 7.35||6.57 || 7.27
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|-
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|2044|| 22.4 ||24.44|| 24.03||21.77 || 20.24
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|-
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|online1||35.6 ||34.66||34.33||31.44 || 30.53
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|-
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|online2||29.6 ||27.23||26.80||24.10 || 23.89
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|-
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|map||24.5|| 27.54||27.69||23.79 || 22.46
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|-
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|notepad||16|| 19.81||21.75||15.81 || 12.74
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|-
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|general||36|| 38.52||38.90||33.61 || 31.55
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|-
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|speedup||26.8||27.88||26.81||22.82 || 22.00
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|-
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|}
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*Tencent baseline is with 700h online data+ 700h 863 data, HLDA+MPE, 88k lexicon
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*Our results are with 400 hour AM, 88k LM. ML+bMMI
  
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2. DNN progress
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===Tencent test result===
  
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  (1) 400 hour BN model.
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: AM: 70h training data(2 day, 15 machines, 10 threads)
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: LM: 88k LM
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    BN-fmmi outperforms the MFCC-fmmi baseline: 13.5%
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:  Test case: general
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    BN-fmmi vs DNN-HMM hybrid: xx%
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:  gmmi-bmmi: 38.7%
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:  dnn-1: 28%  11 frame window,  phone-based tree
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: dnn-2: 34% 9  frame window,  state-based tree
  
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(2) Tencent test result: 70h training data(2 day, 15 machines, 10 threads), 88k LM, general test case:
 
  
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gmmi-bmmi: 38.7%
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===GPU & CPU merge===
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dnn-1: 28%  11 frame window,  phone-based tree
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: Invesigate the possibility to merge GPU and CPU code. Try to find out an easier way. (1 week)
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dnn-2: 34%  9  frame window,  state-based tree
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===L-1 sparse initial training===
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:  Start to investigating.
  
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(2) GPU & CPU merge. Invesigate the possibility to merge GPU and CPU code. Try to find out an easier way. (1 week)
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==Kaldi/HTK merge==
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:* HTK2Kaldi: the tool with Kaldi does not work.
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:* Kaldi2HTK: done with implementation. Testing?
  
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3.Kaldi/HTK merge
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==Embedded progress==
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:* Some large performance (speed) degradation with the embedded platform(1/60).
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(1) HTK2Kaldi: the tool with Kaldi does not work.
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:* Planning for sparse DNN.
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(2) Kaldi2HTK: done with implementation. Testing?
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:* QA LM training, still failed. Mengyuan need more work on this.
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4. Embedded progress
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(1). Some large performance (speed) degradation with the embedded platform(1/60).
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(2). Planning for sparse DNN.
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(3). QA LM training, Mengyuan?
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2013年4月26日 (五) 05:31的最后版本

Data sharing

  • AM/lexicon/LM are shared.
  • LM count files are still in transfering.

DNN progress

400 hour DNN training

Test Set Tencent Baseline bMMI fMMI BN(with fMMI) Hybrid
1900 8.4 7.65 7.35 6.57 7.27
2044 22.4 24.44 24.03 21.77 20.24
online1 35.6 34.66 34.33 31.44 30.53
online2 29.6 27.23 26.80 24.10 23.89
map 24.5 27.54 27.69 23.79 22.46
notepad 16 19.81 21.75 15.81 12.74
general 36 38.52 38.90 33.61 31.55
speedup 26.8 27.88 26.81 22.82 22.00
  • Tencent baseline is with 700h online data+ 700h 863 data, HLDA+MPE, 88k lexicon
  • Our results are with 400 hour AM, 88k LM. ML+bMMI

Tencent test result

AM: 70h training data(2 day, 15 machines, 10 threads)
LM: 88k LM
Test case: general
gmmi-bmmi: 38.7%
dnn-1: 28% 11 frame window, phone-based tree
dnn-2: 34% 9 frame window, state-based tree


GPU & CPU merge

Invesigate the possibility to merge GPU and CPU code. Try to find out an easier way. (1 week)

L-1 sparse initial training

Start to investigating.

Kaldi/HTK merge

  • HTK2Kaldi: the tool with Kaldi does not work.
  • Kaldi2HTK: done with implementation. Testing?

Embedded progress

  • Some large performance (speed) degradation with the embedded platform(1/60).
  • Planning for sparse DNN.
  • QA LM training, still failed. Mengyuan need more work on this.
取自“http://index.cslt.org/mediawiki/index.php?title=2013-04-19&oldid=7503”