“Sinovoice-2016-6-2”版本间的差异

2016年6月2日 (四) 05:09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

• 8k more data
• 50h for training
• 120h labeled ready

• PingAn

• 100h User data done

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:

• Done.

Embedding

• Finish model training. performance/speed/size all looks ok.
• Need further test on embedded device.

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

• Deletion error

• Solved by add a noise phone(spn) to alleviate the silence over-training.

• TDNN silence scale is too sensitive for different test cases.

• After adding "spn", maybe there's no need to tune silence-scale so carefully.

• Stream-mode decoding(cmvn) causes performance reduction.

SiaSun Robot

• Beam-forming algorithm test
• NN-model based beam-forming

SID

Digit

• Engine Package