Sinovoice-2014-03-04 - 版本历史

2014年3月4日 (二) 08:47 Cslt

2014-03-04T08:47:45Z

2014年3月4日 (二) 08:47 Cslt

2014-03-04T08:47:34Z

Cslt：/* Online decoder */

2014-03-04T07:26:53Z

‎Online decoder

2014年3月4日 (二) 07:26 Cslt

2014-03-04T07:26:08Z

2014年3月4日 (二) 07:25 Cslt

2014-03-04T07:25:10Z

2014年3月4日 (二) 07:23 Cslt

2014-03-04T07:23:58Z

Cslt：以内容“=DNN training= ==Environment setting== * Schedule cluster poweroff on 3/06, construct RAID-0 on train212 * The new RAID-0 used three new disks on train212 * Change ...”创建新页面

2014-03-04T07:18:37Z

以内容“=DNN training= ==Environment setting== * Schedule cluster poweroff on 3/06, construct RAID-0 on train212 * The new RAID-0 used three new disks on train212 * Change ...”创建新页面

新页面

=DNN training=

==Environment setting==

* Schedule cluster poweroff on 3/06, construct RAID-0 on train212
* The new RAID-0 used three new disks on train212
* Change nfs names: disk1 -> /nfs/disk212, the raid disks: /nfs/raid212, disk2->/nfs/raid215

==Corpora==

* PICC data are under labeling (200h), ready in one week.
* 105h data from BJ mobile
* 127h Hubei telecom
* Now totally 1121h (470 + 346 + 105 + 200) telephone speech will be ready soon.
* 16k 6000h data: 978h online data from DataTang + 656h online mobile data + 4300h recording data

==Telephone model training==

===470 + 300h + BJ mobile 105h training===

Training condition NO NOISE NOISE in LM opt noise NOISE LM + opt noise

No noise: 30.61% - - -
noise phone added: 31.88% 30.76% 31.27% 31.07

===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%

==6000 hour 16k training==

===Training progress===
* Ran CE DNN to iteration 11 (8400 states, 80000 pdf)
* Testing results go down to 12.49% WER (Sinovoice results: 10.49).

{| class="wikitable"
! Model !! WER !! RT
|-
|small LM, it 4, -5/-9 ||15.80 || 1.18
|-
|large LM, it 4, -5/-9 || 15.30 || 1.50
|-
|large LM, it 4, -6/-9 || 15.36 || 1.30
|-
|large LM, it 4, -7/-9 || 15.25 || 1.30
|-
|large LM, it 5, -5/-9 || 14.17 || 1.10
|-
|large LM, it 5, -5/-10 || 13.77 || 1.29
|-
|large LM, it 6, -5/-9 || 13.64 || 1.12
|-
|large LM, it 6, -5/-10 || 13.25 || 1.33
|-
|large LM, it 7, -5/-9 || 13.29 || 1.12
|-
|large LM, it 7, -5/-10 || 12.87 || 1.17
|-
|large LM, it 8, -5/-9 || 13.09 || -
|-
|large LM, it 8, -5/-10 || 12.69 || -
|-
|large LM, it 9, -5/-9 || 12.87 || -
|-
|large LM, it 9, -5/-10 || 12.55 || -
|-
|large LM, it 10, -5/-9 || 12.83 || 1.51
|-
|large LM, it 10, -5/-10 || 12.48 || 1.65
|-
|large LM, it 11, -5/-9 || 12.87 || 1.61
|-
|large LM, it 11, -5/-10 || 12.46 || 1.28
|-
|large LM, it 12, -5/-9 || 12.91 || 1.61
|-
|large LM, it 12, -5/-10 || 12.49 || 1.28
|-
|}

* xEnt training is done

===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
<pre>
di-syl jt-ph
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%
</pre>

===Hybrid training===
*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%)

==Auto Transcription==

===PICC===

* PICC development set decoding obtained 45% WER.
* PICC auto-trans incremental DT training completed

<pre>
Threshold WER
org: 45.03%
0.9: 41.89%
0.8: 41.64%
</pre>

* Current training data with 0.8 involve 80k sentences, amounting to about 60h data.
* Sampling 60h labelled data to enrich the training
* Prepare to compare the unsupervised incremental training and supervised training

===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

=DNN Decoder=

==Online decoder==
* Various CMN implementation test
:* 200ms/500ms frame block adaptation
:* 10ms frame block adaptation: totally wrong

prior weight -1 1 5 10 20 50 100

200ms 28.29 37.53 35.50 34.08 32.90 32.30 32.77
500ms 28.29 31.28 30.83 30.22 29.50 29.32 29.36

* CMN code delivery
* Online model adaptation

@@ 第155行： / 第155行： @@
 |-
 !prior weight !!   -1  !!     1  !!     5  !!      10  !!    20  !!    50  !!    100
+|-
 |200ms        ||   28.29  ||  37.53  || 35.50 ||  34.08 ||   32.90  || 32.30  || 32.77
 |-

@@ 第24行： / 第24行： @@
 No noise:                                30.61%           -                    -             -
 noise phone added:                       31.88%          30.76%              31.27%         31.07
-<;pre>
+</pre>
 ===BJ mobile incremental training===

@@ 第19行： / 第19行： @@
 ===470 + 300h + BJ mobile 105h training===
+<pre>
 Training condition                    NO NOISE        NOISE in LM       opt noise   NOISE LM + opt noise
 No noise:                                30.61%           -                    -             -
 noise phone added:                       31.88%          30.76%              31.27%         31.07
+<;pre>
 ===BJ mobile incremental training===
 (1) Original 470 + 300 model: 30.24% WER
+<pre>
 MPE2      MPE3         MPE3+iLM       MPE4+iLM
 .01%     26.72%       25.09%         24.53%

@@ 第166行： / 第166行： @@
 |500ms        ||   28.29  ||  31.28  || 30.83 ||  30.22 ||   29.50  || 29.32  || 29.36
 |-
+|}
 * CMN code delivery
 * Online model adaptation

@@ 第98行： / 第98行： @@
 MPE4    14.24%      13.68%
 </pre>
 ===Hybrid training===
@@ 第132行： / 第134行： @@
 * 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
@@ 第137行： / 第140行： @@
 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

@@ 第152行： / 第152行： @@
 :* 10ms frame block adaptation: totally wrong
+{| class="wikitable"
-prior weight    -1       1       5        10      20      50      100
+|-
+!prior weight !!   -1  !!     1  !!     5  !!      10  !!    20  !!    50  !!    100
-ms           28.29    37.53   35.50   34.08    32.90   32.30   32.77
-ms           28.29    31.28   30.83   30.22    29.50   29.32   29.36
+|200ms        ||   28.29  ||  37.53  || 35.50 ||  34.08 ||   32.90  || 32.30  || 32.77
 * CMN code delivery
 * Online model adaptation