2014年3月19日 (三) 03:19的版本

Environment setting

• Raid215 is a bit slow. Move some den-lattice and alignment to Raid212.

Corpora

• PICC data are done (200h).
• Huibei telecom data are done (108h).
• Now totally 1229h (470 + 346 + 105BJ mobile + 200 PICC + 108h) 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

• Xent completed. Compiling lattices.
• Need to test the xEnt performance

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 not very consistent as well
• Xiaoming will try to reproduce the Qihang training using the big database

• Test 1700h model and 6000h model on T test

  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 data is general better than 1700h for careful reading or domain specific recording
• 6000h with MPE/jt phoneset is on training, but better performance is expected
• Suggest test the 6000 model on jidong
• Suggest online test prefers online training data

Hubei telecom

• Hubei telecom data (127 h), retrieve 60k sentence by conf thred=0.9, amounting to 50%

xEnt org:  -             wer_15  29.05
MPE iter1：wer_14 29.23；wer_15 29.38
MPE iter2：wer_14 29.05；wer_15 29.11
MPE iter3：wer_14 29.32；wer_15 29.28
MPE iter4：wer_14 29.29；wer_15 29.28

• retrieve 30k sentences by conf thred=0.95, amounting to 25%, plus the original 770h data

xEnt org:     -             wer_15  29.05
MPE iter1:    -             wer_15: 29.36

• Incremental training with Hubei telecome based on the model (470+300+BJmobile)

• MPE4 modeltraining done: org: 27.30, Hubei model: 25.42

Language modeling

• Training data ready
• Focus on PICC test set

DNN Decoder

Online decoder adaptation

• Finished alignment/den-lattice
• 1st round MPE training on going, 2 days/iteration