Sinovoice-2014-03-11

Environment setting

• Raid212/Raid215/Disk212 done

Corpora

• PICC data are under labeling (200h) done.
• Now totally 1121h (470 + 346 + 105BJ mobile + 200 PICC) telephone speech is ready.
• 16k 6000h data: 978h online data from DataTang + 656h online mobile data + 4300h recording data

Acoustic modeling

1000h Training

• Training recipe prepared
• Start to run.

BJ mobile incremental training

(1) Original 470 + 300 model: 30.24% WER

MPE2      MPE3         MPE3+iLM       MPE4+iLM
27.01%     26.72%       25.09%         24.53%

PICC dedicated training

Baseline (470+300h): 45.03
+ PICC 105h incremental training (th=0.9): 41.89
+ PICC 105h incremental training (th=0.8): 41.64
+ PICC 105h labelled training: 34.78
+ PICC 105h labelled training + PICC text LM: 29.18

6000 hour 16k training

Training progress

• Ran DNN MPE to iteration 5.
• Receipe

• 100h MPE training
• 1700h MPE alignment/lattice
• 1700h MPE training

• 1 week to complete 3 MPE iterations
• MPE2 result: 1e-9: 10.67% (8.61%), 1e-10: 10.34% (8.27%)
• MPE3 result: 1e-9: 10.48% (8.43%), 1e-10: 10.12% (8.05%)
• MPE4 result: 1e-9: 10.34% (8.31%), 1e-10: 10.03% (7.97%)
• MPE5 result:

Training Analysis

• Shared tree GMM model training completed, WER% is similar to non-shared model .
• Selected 100h online data, trained two systems: (1) di-syllable system (2) jt-phone system

        di-syl      jt-ph
GMM:      -         20.86%
Xent    15.42%      14.78%       
MPE1    14.46%      14.23%
MPE2    14.22%      14.09%
MPE3    14.26%      13.80%
MPE4    14.24%      13.68%

• HTK training on the same database

• HLDA: 18.22
• HLDA+MPE: 14.40

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

Language modeling

• Need transfer the training text

DNN Decoder

Online decoder

• CMN code delivered. Integration is done
• CMN pipe code delivered. Model adaptation is on going