“2013-04-19”版本间的差异
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| − | + | ==Data sharing== | |
| − | + | * AM/lexicon/LM are shared. | |
| + | * LM count files are still in transfering. | ||
| − | + | ==DNN progress== | |
| + | ===400 hour DNN training=== | ||
| + | {| class="wikitable" | ||
| + | !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. | |
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2013年4月26日 (五) 05:31的最后版本
目录
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.
