“Sinovoice-2016-4-28”版本间的差异

2016年4月28日 (四) 04:54的版本

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
• H smoothing of XEnt and MPE

• Testdata: test_1000ju from 8000ju

  -----------------------------------------------------------------------------
                 model                    | ins  |  del  | sub | wer/tot-err  
  -----------------------------------------------------------------------------
   svd600_lr2e-5_1000H_mpe_uv-fix         |  24  |  56   | 408 | 8.26/488
  -----------------------------------------------------------------------------
svd600_lr2e-5_1000H_mpe_uv-fix_omitsilacc |  32  |  48   | 409 | 8.28/489
  -----------------------------------------------------------------------------
   svd600_lr2e-5_1000H_mpe_uv-fix_xent0.1 |  24  |  57   | 406 | 8.24/487
  -----------------------------------------------------------------------------

• Testdata: test_8000ju

  -----------------------------------------------------------------------------
                 model                    | ins  |  del  | sub  | wer/tot-err  
  -----------------------------------------------------------------------------
   svd600_lr2e-5_1000H_mpe_uv-fix         |  140 |  562  | 3686 | 9.19/4388     | 47753-total-word
  -----------------------------------------------------------------------------
   svd600_lr2e-5_1000H_mpe_uv-fix_xent0.1 |  146 |  510  | 3705 | 9.13/481
  -----------------------------------------------------------------------------

• Testdata: test_2000ju from 10000ju

  -----------------------------------------------------------------------------
                 model                    | ins  |  del  |  sub | wer/tot-err  
  -----------------------------------------------------------------------------
   svd600_lr2e-5_1000H_mpe_uv-fix         |  86  |  790  | 1471 | 18.55/2347
  -----------------------------------------------------------------------------
svd600_lr2e-5_1000H_mpe_uv-fix_omitsilacc |  256 |  473  | 1669 | 18.95/2398
  -----------------------------------------------------------------------------
   svd600_lr2e-5_1000H_mpe_uv-fix_xent0.1 |  95  |  704  | 1548 | 18.55/2347
  -----------------------------------------------------------------------------

• Testdata: test_10000ju

  -----------------------------------------------------------------------------
                 model                    | ins  |  del  | sub  | wer/tot-err  
  -----------------------------------------------------------------------------
   svd600_lr2e-5_1000H_mpe_uv-fix         |  478 | 3905  | 7698 | 18.31/12081  | 65989-total-word
  -----------------------------------------------------------------------------
   svd600_lr2e-5_1000H_mpe_uv-fix_xent0.1 |  481 | 3741  | 7773 | 18.18/11995
  -----------------------------------------------------------------------------

• 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

 PingAn:
 ===============================================================================
 |     AM / config            |   all beam9   |all beam9 biglm||  KeHu beam9   |
 -------------------------------------------------------------------------------
 | tdnn 7-2048 xEnt           |     16.45     |     16.22     || 36.49 / 25.18 |
 | tdnn 7-2048 MPE            |     15.22     |     14.87     || 32.77 / 23.48 |
 | tdnn 7-2048 MPE adapt-PABX |     14.67     |     14.63     || 31.33 / 22.76 |
 -------------------------------------------------------------------------------
 | tdnn 7-1024 xEnt           |     16.60     |     16.25     || 35.91 / 25.58 |
 | tdnn 7-1024 MPE            |     15.67     |     15.61     || 32.77 / 26.09 |
 | tdnn 7-1024 MPE adapt-PABX |     14.80     |     14.76     || 30.48 / 22.56 |
 ===============================================================================

 LiaoNingYiDong:
 ==============================================================================
 |     AM / config            |     beam9     |   beam9 biglm |     beam13    |
 ------------------------------------------------------------------------------
 | tdnn 7-2048 xEnt           |     21.51     |     21.05     |     21.17     |
 | tdnn 7-2048 MPE            |     20.09     |     19.74     |     19.74     |
 | tdnn 7-2048 MPE adapt-LNYD |     17.92     |     17.87     |     17.58     |
 ------------------------------------------------------------------------------
 | tdnn 7-1024 xEnt           |     21.72     |     22.74     |     21.64     |
 | tdnn 7-1024 MPE            |     20.99     |     20.77     |     20.74     |
 | tdnn 7-1024 MPE adapt-LNYD |               |               |               |
 ==============================================================================

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)