Sinovoice-2016-4-21

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Data

  • 16K LingYun
  • 2000h data ready
  • 4300h real-env data to label
  • YueYu
  • Total 250h(190h-YueYu + 60h-English)
  • Add 60h YueYu
  • CER: 75%->76%
  • WeiYu
  • 50h for training
  • 120h labeled ready

Model training

Deletion Error Promblem

  • Add one noise phone to alleviate the silence over-training
  • Omit sil accuracy in discriminative training
  • Testdata: test_1000ju from 8000ju
  ---------------------------------------------------
                 model   | ins  |  del  | sub | wer  
  ---------------------------------------------------
         baseMPE 3.mdl   |  25  |  68   | 468 | 9.50
  ---------------------------------------------------
   MPE omit_sil_acc 3.mdl|  26  |  72   | 453 | 9.33
  ---------------------------------------------------
  • Testdata: test_2000ju from 10000ju
  ----------------------------------------------------
                 model   | ins  |  del  | sub  | wer  
  ----------------------------------------------------
         baseMPE 3.mdl   |  96  |  768  | 1590 | 19.39
  ----------------------------------------------------
  MPE omit_sil_acc 3.mdl |  165 |  627  | 1685 | 19.58
  ----------------------------------------------------
  • H smoothing of XEnt and MPE
  • Add one silence arc from start-state to end-state

Big-Model Training

  • 7*2048-10000h net weight-matrix factoring, to improve the decoding speed --SVD
  • SVD looks OK, but fine-tuning still didn't work.
 Base WER:
 relu_2000_mpe_1000H: 17.72
 relu_1200_mpe_1000H: 18.60
 |layer / nodes retaind|  200  |  400  |  600  |  800  | 1000  | 1200  | 1400  |  1600  |
 |      hidden 2       |       |       | 22.53 | 20.30 | 19.01 |       |       |        |
 |      hidden 7       |       | 18.92 | 18.30 | 17.92 |       |       |       |        |     
 |       final         |       |       | 18.32 | 18.00 | 17.83 |       |       |        |     
  • 7*1024 cross-entropy total train, then mpe, 0.2 improvment
  • 7*1024 svd factoring, speed the decoding
  • 8k

Embedding

  • 10000h-chain 5*400+800 DONE.
  • Beam affect the performance of chain model significantly, need more investigation.
  • 5*576-2400 TDNN model

SinSong Robot

  • Test based on 10000h(7*2048-xent) model
 ------------------------------------------------
   condition | clean  | replay(0.5m) | real-env
 ------------------------------------------------
     wer     |   3    |  18(mpe-14)  | too-bad
 ------------------------------------------------
  • Plan to record in restaurant on April 10.

Character LM

  • Except Sogou-2T, 9-gram has been done.
  • Worse than word-lm(9%->6%)
  • Add word boundary tag to Character-LM trainig
  • Merge Character-LM & word-LM
  • Union
  • Compose, success.
  • 2-step decoding: first, character-based LM. Then, word-based LM.

Project

  • Pingan & Yueyu Deletion error too more
  • TDNN deletion error rate > DNN deletion error rate
  • TDNN Silence scale is too sensitive for different test cases.

SID

Digit

  • Same Channel test EER: 100%
  • Speaker confirm
  • phone channel
  • Cross Channel
  • Mic-wav PLDA adaptation EER from 9% to 7% (20-30 persons)