View source for Sinovoice-2016-5-26

==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
:* 8k more data
:* 50h for training
:* 120h labeled ready

* PingAn
:*100h User data done

==Model Training==
===Deletion Error Problem===
* Add one noise phone to alleviate the silence over-training, looks OK.
* Omit sil accuracy in discriminative training
* H smoothing of XEnt and MPE, no significant affect.
* Add one silence arc from start-state to end-state

===Big-Model Training===
====16k====
* Done!
====8k=====

=====Project=====
* PingAn

  =========================================================================================
  |     AM / config                     |      all      |    KeHu wer   ||  KeHu no-ins  |
  -----------------------------------------------------------------------------------------
  | tdnn 7-2048 xEnt                    |     16.45     |     36.49     ||     25.18     |
  | tdnn 7-2048 MPE                     |     15.22     |     32.77     ||     23.48     |
  | tdnn 7-2048 MPE adapt-PABX          |     14.67     |     31.33     ||     22.76     |
  -----------------------------------------------------------------------------------------
  | tdnn 7-1024 xEnt                    |     16.60     |     35.91     ||     25.58     |
  | tdnn 7-1024 MPE 2e-6                |     15.67     |     32.77     ||     26.09     |
  | tdnn 7-1024 MPE 2e-5 1.mdl          |     15.54     |     32.77     ||     26.29     |
  | tdnn 7-1024 MPE 1e-5 4.mdl          |     15.76     |     33.55     ||     27.20     |
  | tdnn 7-1024 MPE adapt-PABX          |     14.80     |     30.48     ||     22.56     |
  -----------------------------------------------------------------------------------------
  | spn 7-1024 xEnt                     |     16.49     |     36.23     ||     24.59     |
  | spn 7-1024 xEnt xEnt-PA_user 101.mdl|     16.19     |     33.22     ||     22.69     |
  | spn 7-1024 xEnt xEnt-PA_user mpe    |     15.24     |     32.77     ||     21.65     |
  | spn 7-1024 MPE-1000H 23.mdl         |     15.29     |     33.09     ||     21.65     |
  | spn 7-1024 MPE adapt-PA_all 29.mdl  |     15.11     |     33.42     ||     21.84     |
  | spn 7-1024 MPE adapt-PA_user 2e-5   |     15.31     |     31.79     ||     20.14     |
  | spn 7-1024 MPE adapt-PA_user Hs 2e-5|     15.32     |     32.24     ||     20.93     |
  =========================================================================================

  =====================================================================================================
  |     LM / config                           |     KeHu      |KeHu check_zxm_recheck|  KeHu final   |
  -----------------------------------------------------------------------------------------------------
  |     baseline                              |     20.14     |         19.40        |     18.26     |
  -----------------------------------------------------------------------------------------------------
  |bank+baoxian.chart+word.w0.9               |       -       |         19.27        |     17.88     |
  |bank+baoxian+guojiadianwang.chart+word.w0.9|       -       |         19.02        |     17.94     |
  |bank+baoxian+guojiadianwang_w0.9           |       -       |         19.02        |       -       |
  |bank+baoxian_w0.9                          |       -       |         19.20        |       -       |
  |baoxian+bank_w0.9                          |       -       |         19.02        |       -       |
  |baoxian+user200h.chart+word.w0.9           |       -       |         19.20        |       -       |
  |baoxian+user200h_w0.8                      |       -       |         19.02        |       -       |
  |baoxian+user200h_w0.9                      |       -       |         19.08        |     18.01     |
  |baoxian+user200h.w0.9w0.9                  |       -       |         19.08        |       -       |
  |baoxian+user200h.chart1e-7.w0.9w0.1        |       -       |           -          |     23.82     |
  |baoxian+user200h.chart1e-7.w0.9w0.9        |       -       |           -          |     18.26     |
  =====================================================================================================


* LiaoNingYiDong:
  =========================================================================
  |     AM / config                     |     LNYD      |  LNYD re-tag  |
  -------------------------------------------------------------------------
  | tdnn 7-2048 xEnt                    |     21.51     |               |
  | tdnn 7-2048 MPE                     |     20.09     |               |
  | tdnn 7-2048 MPE adapt-LNYD          |     17.92     |     16.29     |
  -------------------------------------------------------------------------
  | tdnn 7-1024 xEnt                    |     21.72     |               |
  | tdnn 7-1024 MPE                     |     20.99     |               |
  | cnn 7-1024 xEnt 600.mdl             |     21.03     |               |
  | cnn 7-1024 MPE 12.mdl               |     19.80     |               |
  | cnn 7-1024 MPE adapt-LNYD 41.mdl    |     17.96     |     15.93     |
  -------------------------------------------------------------------------
  | spn 7-1024 xEnt                     |     21.70     |               |
  | spn 7-1024 MPE-1000H 23.mdl         |     19.97     |               |
  | spn 7-1024 MPE adapt-LNYD           |     18.67     |               |
  | spn cnn 7-1024 xEnt 300.mdl         |     22.26     |               |
  ========================================================================

===Embedding===
* The size of nnet1 AM is 6.4M (3M after decomposition). So we need to control AM size within 10M.
* 5*500-2400 TDNN no-svd/svd100 model, MPE training done

 LM=1e-5, beam=9, max-active=5000
  =============================================================================================================
  |         AM / testset              |  test_1000ju  |  test_2000ju  |  test_8000ju  |  test_10000ju  |
  -------------------------------------------------------------------------------------------------------------
  | nnet1 4*600+800 xEnt (6.4M)       |     25.30     |     40.48     |               |                |
  | nnet1 4*600+800 mpe  (6.4M)       |     20.75     |     35.33     |               |                |
  -------------------------------------------------------------------------------------------------------------
  | nnet3 5*500 mpe (13M)             |     16.18     |     29.53     |               |                |
  | nnet3 5*500 svd-100 mpe (9.5M)    |     17.69     |     30.11     |               |                |
  =============================================================================================================

===Character LM===
* Except Sogou-2T, 9-gram has been done.
* Add word boundary tag to Character-LM trainig done
:* 9-gram
:* Except Weibo & Sogou-2T
:* 1e-7(13M) wer17.91 compared with 1e-7(no-boundary,71M) 13.4
:* 1e-8(54M) wer17.54

* Prepare specific domain vocabulary
:* Dianxin/Baoxian/Dianli

*DT lm training
:* ReFr

*Merge Character-LM  & word-LM
:* Union
:* Compose, success.
* 2-step decoding: first, character-based LM. Then, word-based LM.

===Problem===
* Pingan & Yueyu too much deletion error.
:* TDNN deletion error rate > DNN deletion error rate
:* TDNN Silence scale is too sensitive for different test cases.
* cmvn causes performance reduction.

==SiaSun Robot==
* Beam-forming algorithm test
* NN-model based beam-forming

==SID==
===Digit===
* Engine Package