“Sinovoice-2014-03-25”版本间的差异
来自cslt Wiki
（以内容“=Environment setting= * Raid215 is a bit slow. Move some den-lattice and alignment to Raid212. =Corpora= * Labeling Beijing Mobile? * Now totally 1229h (470 + 346 +...”创建新页面） |
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*Baseline: 8k states, 470+300 MPE4, 20.29 | *Baseline: 8k states, 470+300 MPE4, 20.29 | ||
| − | *MPE1: 21.91 | + | :*MPE1: 21.91 |
| − | *MPE2: 21.71 | + | :*MPE2: 21.71 |
| − | *MPE3: 21.68 | + | :*MPE3: 21.68 |
| − | *MPE4: 21.86 | + | :*MPE4: 21.86 |
| − | * CSLT phone, 8k states | + | * CSLT phone, 8k states training |
| − | *MPE1: 20.60 | + | :*MPE1: 20.60 |
| − | *MPE2: 20.37 | + | :*MPE2: 20.37 |
===PICC dedicated training=== | ===PICC dedicated training=== | ||
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:* Seems the database is still not very consistent | :* Seems the database is still not very consistent | ||
:* Xiaoming will try to reproduce the Qihang training using this subset | :* Xiaoming will try to reproduce the Qihang training using this subset | ||
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:* Suggest test the 6000 model on jidong data | :* Suggest test the 6000 model on jidong data | ||
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* Training data ready | * Training data ready | ||
| − | * | + | * Xiaoxi from CSLT may be involved after some patent writing |
=DNN Decoder= | =DNN Decoder= | ||
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stream: MPE2: %WER 9.48 [ 4529 / 47753, 251 ins, 477 del, 3801 sub ] | stream: MPE2: %WER 9.48 [ 4529 / 47753, 251 ins, 477 del, 3801 sub ] | ||
stream: MPE3: %WER 9.43 [ 4502 / 47753, 230 ins, 484 del, 3788 sub ] | stream: MPE3: %WER 9.43 [ 4502 / 47753, 230 ins, 484 del, 3788 sub ] | ||
| − | stream: MPE4: | + | stream: MPE4: %WER 9.39 [ 4482 / 47753, 236 ins, 475 del, 3771 sub ] |
</pre> | </pre> | ||
2014年3月25日 (二) 06:10的版本
目录
Environment setting
- Raid215 is a bit slow. Move some den-lattice and alignment to Raid212.
Corpora
- Labeling Beijing Mobile?
- Now totally 1229h (470 + 346 + 105BJ mobile + 200 PICC + 108h HBTc) telephone speech is ready.
- 16k 6000h data: 978h online data from DataTang + 656h online mobile data + 4300h recording data.
- LM corpus preparation done.
Acoustic modeling
Telephone model training
1000h Training
- Jietong phone, 200 hour seed, 10k states training:
- Baseline: 8k states, 470+300 MPE4, 20.29
- MPE1: 21.91
- MPE2: 21.71
- MPE3: 21.68
- MPE4: 21.86
- CSLT phone, 8k states training
- MPE1: 20.60
- MPE2: 20.37
PICC dedicated training
- Need to collect financial text data and retrain the LM
- Need to comb word list and training text
6000 hour 16k training
Training progress
- 6000h/CSLT phone set alignment/denlattice completed
- 6000h/jt phone set alignment/denlattice completed
- MPE is kicked off
Train Analysis
- The Qihang model used a subset of the 6k data
- 2500+950H+tang500h*+20131220, approximately 1700+2400 hours
- GMM training using this subset achieved 22.47%. Xiaoming's result is 16.1%.
- Seems the database is still not very consistent
- Xiaoming will try to reproduce the Qihang training using this subset
- Suggest test the 6000 model on jidong data
Language modeling
- Training data ready
- Xiaoxi from CSLT may be involved after some patent writing
DNN Decoder
Online decoder adaptation
- Incremental training finished (stream mode)
- 8k sentence test
non-stream baseline MPE5: %WER 9.91 [ 4734 / 47753, 235 ins, 509 del, 3990 sub ] stream: MPE1：%WER 9.66 [ 4612 / 47753, 252 ins, 490 del, 3870 sub ] stream: MPE2: %WER 9.48 [ 4529 / 47753, 251 ins, 477 del, 3801 sub ] stream: MPE3: %WER 9.43 [ 4502 / 47753, 230 ins, 484 del, 3788 sub ] stream: MPE4: %WER 9.39 [ 4482 / 47753, 236 ins, 475 del, 3771 sub ]
