Sinovoice-2014-03-25

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 traiing
• 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

• Tested the 1700h model and 6000h model on the T test sets

  model/testcase  |   ditu |  due1| entity1 | rec1 | shiji | zaixian1 | zaixian2 | kuaisu
  ------------------------------------------------------------------------------------------------
    1700h_mpe       |  12.18 | 12.93 | 5.29   |   3.69     |  21.73  | 25.38   | 19.45   | 12.50
  ------------------------------------------------------------------------------------------------
    6000h_xEnt      |  11.13 | 10.12 | 4.64   |   2.80     |  17.67  | 27.45   | 23.23   | 10.98 

• 6000h model is general better than the 1700h for careful reading or domain specific recording
• 6000h with MPE/jt phone set is still on training, but better performance is expected
• This indicates that we should prepare domain-specific AM (not only 8k/16k). The online test prefers online training data
• Suggest test the 6000 model on jidong data

Language modeling

• Training data ready
• First focus on PICC test set, try to improve the PPL

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 ]