“2014-11-10”版本间的差异

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第1行： 第1行：
 +
==Speech Processing ==
 +
=== AM development ===
 +
 +
==== Environment ====
 +
* Already buy 3 760GPU
 +
* grid-9 760GPU crashed again
 +
 +
==== Sparse DNN ====
 +
* Performance improvement found when pruned slightly
 +
* The result of AURORA 4 will be available soon.
 +
* details at http://liuc.cslt.org/pages/sparse.html
 +
 +
==== RNN AM====
 +
* Initial nnet seems not very well, need to be pre-trained or test lower learn-rate.
 +
* For AURORA 4 1h/epoch, model train done.
 +
* Using AURORA 4 short-sentence with a smaller number of targets.(+)
 +
* Adjusting the learning rate.(+)
 +
* Trying toolkit of Microsoft.(+)
 +
* details at http://liuc.cslt.org/pages/rnn.html
 +
 +
==== A new nnet training scheduler ====
 +
* Initial code done. No better than original one considering of taking much more iterations.
 +
* details at http://liuc.cslt.org/pages/nnet-sched.html
 +
 +
====Noise training====
 +
* Paper has been submitted.
 +
 +
====Drop out & Rectification & convolutive network====
 +
 +
* Drop out
 +
:* dataset:wsj, testset:eval92
 +
        std |  dropout0.4 | dropout0.5 | dropout0.6 | dropout0.7 | dropout0.7_iter7(maxTr-Acc) | dropout0.8 | dropout0.8_iter7(maxTr-Acc)
 +
    ------------------------------------------------------------------------------------------------------------------------------------
 +
        4.5 |    5.39    |    4.80    |  4.75    |  4.36      |  4.39                      |    4.55    |    4.71         
 +
:** Frame-accuarcy seems not consistent with WER. Using the train-data as cv, verify the learning ability of the model.
 +
    Seems in one nnet model the train top frame accuracy is not consistent with the WER.
 +
:** Decode test_clean_wv1 dataset. 
 +
 +
:* AURORA4 dataset
 +
 +
  (1) Train: train_nosiy
 +
    drop-retention/testcase(WER) | test_clean_wv1  | test_airport_wv1 | test_babble_wv1 | test_car_wv1
 +
    ---------------------------------------------------------------------------------------------------------
 +
          std-baseline          |  9.60          |  11.41          |  11.63          |  8.64
 +
    ---------------------------------------------------------------------------------------------------------
 +
              dp-0.3            |  12.91          |  16.55          |  15.37          |  12.60
 +
    ---------------------------------------------------------------------------------------------------------
 +
              dp-0.4            |  11.48          |  14.43          |  13.23          |  11.04
 +
    ---------------------------------------------------------------------------------------------------------
 +
              dp-0.5            |  10.53          |  13.00          |  12.89          |  10.24
 +
    ---------------------------------------------------------------------------------------------------------
 +
              dp-0.6            |  10.02          |  12.32          |  11.81          |  9.29
 +
    ---------------------------------------------------------------------------------------------------------
 +
              dp-0.7            |  9.65          |  12.01          |  12.09          |  8.89
 +
    ---------------------------------------------------------------------------------------------------------
 +
              dp-0.8            |  9.79          |  12.01          |  11.77          |  8.91
 +
    ---------------------------------------------------------------------------------------------------------
 +
              dp-1.0            |  9.94          |  11.33          |  12.05          |  8.32
 +
    ---------------------------------------------------------------------------------------------------------
 +
      baseline_dp0.4_lr0.008    |  9.52          |  12.01          |  11.75          |  9.44
 +
  ---------------------------------------------------------------------------------------------------------
 +
      baseline_dp0.4_lr0.0001    |  9.92          |  14.22          |  13.59          |  10.24
 +
  ---------------------------------------------------------------------------------------------------------
 +
      baseline_dp0.4_lr0.00001  |  9.06          |  13.27          |  13.14          |  9.33
 +
  ---------------------------------------------------------------------------------------------------------
 +
      baseline_dp0.8_lr0.008    |  9.16          |  11.23          |  11.42          |  8.49
 +
  ---------------------------------------------------------------------------------------------------------
 +
      baseline_dp0.8_lr0.0001    |  9.22          |  11.52          |  11.77          |  8.82
 +
  ---------------------------------------------------------------------------------------------------------
 +
      baseline_dp0.8_lr0.00001  |  9.12          |  11.27          |  11.65          |  8.68
 +
  ---------------------------------------------------------------------------------------------------------
 +
        dp-0.4_follow-std-lr    |  11.33          |  14.60          |  13.50          |  10.95
 +
  ---------------------------------------------------------------------------------------------------------
 +
        dp-0.8_follow-std-lr    |  9.77          |  12.01          |  11.79          |  8.93
 +
  ---------------------------------------------------------------------------------------------------------
 +
          dp-0.4_4-2048          |  11.69          |  16.13          |  14.24          |  11.98
 +
  ---------------------------------------------------------------------------------------------------------
 +
          dp-0.8_4-2048          |  9.46          |  11.60          |  11.98          |  8.78
 +
  ---------------------------------------------------------------------------------------------------------
 +
 +
:** Test with AURORA4 of 7000 (clean + noisy).
 +
:** Follow the standard DNN training learn-rate to avoid the different learn-rate changing time of various DNN training. Similar performance is obtained.
 +
:** Find and test unknown noise test-data.(+)
 +
:** Have done the droptout on normal trained XEnt NNET , eg wsj(learn-rate:1e-4/1e-5). Seems small learn-rate get the balance of accuracy and train-time.
 +
:** Draft the dropout-DNN weight distribution. (++)
 +
 +
* Rectification
 +
:* Combine drop out and rectifier.(+)
 +
:* Change the learn-rate in the middle of the training, Modify the train_nnet.sh script(Liu Chao).
 +
 +
* MaxOut
 +
:* 6min/epoch
 +
1) AURORA4 -15h
 +
    NOTE: gs==groupsize
 +
  (1) Train: train_clean
 +
        model/testcase(WER)    | test_clean_wv1  | test_airport_wv1 | test_babble_wv1 | test_car_wv1
 +
    ---------------------------------------------------------------------------------------------------------
 +
          std-baseline        |  6.04          |  29.91          |  27.76          |  16.37
 +
    ---------------------------------------------------------------------------------------------------------
 +
          lr0.008_gs6          |                            -
 +
    ---------------------------------------------------------------------------------------------------------
 +
          lr0.008_gs10          |                            -
 +
    ---------------------------------------------------------------------------------------------------------
 +
          lr0.008_gs20          |                            -
 +
    ---------------------------------------------------------------------------------------------------------
 +
      lr0.008_l1-0.01          |                            -
 +
    ---------------------------------------------------------------------------------------------------------
 +
        lr0.008_l1-0.001        |                            -
 +
    ---------------------------------------------------------------------------------------------------------
 +
      lr0.008_l1-0.0001        |                            -
 +
    ---------------------------------------------------------------------------------------------------------
 +
    lr0.008_l1-0.000001        |                            -
 +
    ---------------------------------------------------------------------------------------------------------
 +
        lr0.008_l2-0.01        |                            -
 +
    ---------------------------------------------------------------------------------------------------------
 +
            lr0.006_gs10        |                            -
 +
    ---------------------------------------------------------------------------------------------------------
 +
            lr0.004_gs10        |                            -
 +
    ---------------------------------------------------------------------------------------------------------
 +
          lr0.002_gs10        |  6.21          |  28.48          |  27.30          |  16.37
 +
    ---------------------------------------------------------------------------------------------------------
 +
          lr0.001_gs1          |                            -
 +
    ---------------------------------------------------------------------------------------------------------
 +
          lr0.001_gs2          |                            -
 +
    ---------------------------------------------------------------------------------------------------------
 +
          lr0.001_gs4          |                            -
 +
    ---------------------------------------------------------------------------------------------------------
 +
          lr0.001_gs6          |  6.04          |  25.17          |  24.31          |  14.19
 +
    ---------------------------------------------------------------------------------------------------------
 +
          lr0.001_gs8          |  5.85          |  25.72          |  24.35          |  14.28
 +
    ---------------------------------------------------------------------------------------------------------
 +
          lr0.001_gs10        |  6.23          |  27.04          |  25.51          |  14.22
 +
    ---------------------------------------------------------------------------------------------------------
 +
          lr0.001_gs15        |  5.94          |  30.10          |  27.53          |  19.00
 +
    ---------------------------------------------------------------------------------------------------------
 +
          lr0.001_gs20        |  6.32          |  28.10          |  26.47          |  16.98
 +
    ---------------------------------------------------------------------------------------------------------
 +
 +
* P-norm
 +
 +
* Convolutive network (+)
 +
:* AURORA 4
 +
                  |  wer | hid-layers | hid-dim | delta-order | splice | lda-dim | learn-rate | pooling | TBA
 +
-----------------------------------------------------------------------------------------------------------------------
 +
  cnn_std_baseline| 6.70 |    4      | 1200 |      0      |    4  |  198  |  0.008 |  3    |patch-dim1 7
 +
-----------------------------------------------------------------------------------------------------------------------
 +
  cnn_std_1000_3  | 6.61 |    4      | 1000 |      0      |    4  |  198  |  0.008 |  3    |patch-dim1 7
 +
-----------------------------------------------------------------------------------------------------------------------
 +
  cnn_std_1400_3  | 6.61 |    4      | 1400 |      0      |    4  |  198  |  0.008 |  3    |patch-dim1 7
 +
-----------------------------------------------------------------------------------------------------------------------
 +
  cnn_std_1200_4  | 6.91 |    4      | 1200 |      0      |    4  |  198  |  0.008 |  4    |patch-dim1 6
 +
-----------------------------------------------------------------------------------------------------------------------
 +
  cnn_std_1200_2  | -    |    4      | 1200 |      0      |    4  |  198  |  0.008 |  2    |patch-dim1 8
 +
-----------------------------------------------------------------------------------------------------------------------
 +
  cnn_std_1200_3  | 6.66 |    5      | 1200 |      0      |    4  |  198  |  0.008 |  3    |patch-dim1 7
 +
-----------------------------------------------------------------------------------------------------------------------
 +
 +
:* READ paper
 +
 +
====Denoising & Farfield ASR====
 +
* ICASSP paper submitted.
 +
* HOLD
 +
 +
====VAD====
 +
* Frame energy feature extraction, done
 +
* Harmonics and Teager energy features being investigation
 +
* Previous results to be organized for a paper 
 +
 +
====Speech rate training====
 +
* 100h random select from 1000h tec dataset
 +
:* baseline and ROS NNet train done, will decoding soon
 +
* Seems ROS model is superior to the normal one with faster speech
 +
 +
==== low resource language AM training ====
 +
* HOLD
 +
* Uyghur language model has been released to JT. Done.
 +
 +
====Scoring====
 +
* Timber Comparison on testing
 +
 +
====Confidence====
 +
* Reproduce the experiments on fisher dataset.
 +
* Use the fisher DNN model to decode all-wsj dataset
 +
* preparing scoring for puqiang data
 +
 +
===Speaker ID===
 +
* Preparing GMM-based server.
 +
* EER ~ 11.2% (GMM-based system)
 +
* test different number of components; fast i-vector computing
 +
 +
===Language ID===
 +
* GMM-based language is ready.
 +
* Delivered to Jietong
 +
 +
===Emotion detection===
 +
 +
* Sinovoice is implementing the server
 +
 +
 
==Text Processing==
 
==Text Processing==
 
===LM development===
 
===LM development===
第4行： 第203行：
 
====Domain specific LM====
 
====Domain specific LM====
 
* domain lm
 
* domain lm
−
:* weibo lm with pruning 0 10 10 20 20 testing done. weibo lm with pruning 0 10 8 8 8 under testing. weibo lm without pruning 4/8 done.
+
:* merger weibo、baiduhi and baiduzhidao lm and test ('''need result''')
−
:* merger weibo、baiduhi and baiduzhidao lm and test ('''this week''')
+
 
:* confirm the size of alpa with xiaomin for business application.(like e-13)
 
:* confirm the size of alpa with xiaomin for business application.(like e-13)
 
:* get the general test data from miaomin .this test set may get from online.
 
:* get the general test data from miaomin .this test set may get from online.
 +
:* trained a new lm: mobile
 +
:* find the optimal lambda for interpolating following LMs: baidu_hi, mobile, sichuanmobile
 +
:* train some more LMs with Zhenlong
 +
:* keep on training sogou2T lm
 +
  
 
*  new dict.
 
*  new dict.
第21行： 第224行：
  
 
* set new test  
 
* set new test  
−
:* result
+
:*  
 +
{| border="2px"
 +
|+ no "北京" in corpus with tag-lm
 +
|-
 +
! method !!baeline  !! weight0.1 !! weight0.5 !! weight1 !! weight2 !! weight3
 +
|-
 +
! wer
 +
| 56.58 || 69.49 || 62.23 || 58.03 || 56.90 || -
 +
|-
 +
!"北京"
 +
|6/10||4/10||4/10||2/10||1/10||0
 +
|-
 +
! detail
 +
|288 ins, 5075 del, 3178 sub||196 ins, 6016 del, 4278 sub||190 ins, 5870 del, 3334 sub||243 ins, 5294 del, 3223 sub||344 ins, 4558 del, 3687 sub||-
 +
|-
 +
|}
  
 +
* mix seed lm with big lm, test address-tag on big lm
  
 
====RNN LM====
 
====RNN LM====
第58行： 第277行：
  
 
===QA===
 
===QA===
 +
deatil:[http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/Hulan-2014-11-06]
 +
====Spell mistake====
 +
:* retrain the ngram model('''caoli''')
 +
:* prepare the test and development set('''caoli''')
 +
 +
====improve fuzzy match====
 +
* add Synonyms similarity using MERT-4 method
 +
 +
====improve lucene search====
 +
* our vsm method
 +
{| border="2px"
 +
|+ different result in lucene
 +
|-
 +
! method !!lucene  !! vsm_idf(haiguan) !! VSM_idf(baidu) !! vsm_idf(tain) !! vsm_idf(calculate)
 +
|-
 +
! Accary
 +
| 0.6628 || 0.6228 || 0.6197 || 0.5827 || 0.5426
 +
|-
 +
|}
 +
* lucene top
 +
:* top10(82.95%),top20(86.34),top50(90.23%),top100(94.11%),top200(96.18%),top1000(97.31%),top2000(97.87%),top5000(98.75%),top10000(99.06)
 +
:* test the result of top(100,200,1000) in full qa(lucene+fuzzymatch)('''caoli''')
 +
 +
* lucene Optimization(liurong)
 +
:* rewrite the method to select the 50 standard question not same template.(liurong)
 +
:* check the word segment for template.(liurong)
 +
:* boost the query keyword using IDF
 +
{| border="2px"
 +
|+ boost keyword  in lucene
 +
|-
 +
! method !!Default  !! idf_train !! idf_train_norm!! idf_baidu !! idf_baidu_norm
 +
|-
 +
! Accary
 +
| 0.66228 ||  0.651629 ||0.57644|| 0.647869|| 0.65288
 +
|-
 +
|}
 +
:* using MERT-4 method to get good value of multi-feature.like IDF,NER,baidu_weight,keyword etc.('''liurong this month''')
  
−
* lucene Optimization
+
====Multi-Scene Recognition====
−
:* rewrite the method to select the 50 standard question not same template.('''this week''')
+
* add the triples search to QA engine
−
:* test the boost keyword weight and extract the synonyms word.('''this week''')
+
:* discuss the detail and give a report.('''liurong''')
−
:* check the word segment for template.('''this week''')
+
* demo ('''liurong two week''')
−
:* min-segment method improve the accuracy.(0.61->0.66)
+
.
−
:* check the query method for getting lucene information and to rewrite the score method like the idf value.
+
−
* test
+
−
:* test the different idf vale from baidu sougou in fuzzymatch.('''this week''')
+
−
:* need to check the other 10% error.('''this week''')
+
−
* spell check
+
−
:* simple demo done.
+
 
* new inter will install SEMPRE
 
* new inter will install SEMPRE

2014年11月11日 (二) 08:06的最后版本

Speech Processing

AM development

Environment

  • Already buy 3 760GPU
  • grid-9 760GPU crashed again

Sparse DNN

RNN AM

  • Initial nnet seems not very well, need to be pre-trained or test lower learn-rate.
  • For AURORA 4 1h/epoch, model train done.
  • Using AURORA 4 short-sentence with a smaller number of targets.(+)
  • Adjusting the learning rate.(+)
  • Trying toolkit of Microsoft.(+)
  • details at http://liuc.cslt.org/pages/rnn.html

A new nnet training scheduler

Noise training

  • Paper has been submitted.

Drop out & Rectification & convolutive network

  • Drop out
  • dataset:wsj, testset:eval92 
       std |  dropout0.4 | dropout0.5 | dropout0.6 | dropout0.7 | dropout0.7_iter7(maxTr-Acc) | dropout0.8 | dropout0.8_iter7(maxTr-Acc)
    ------------------------------------------------------------------------------------------------------------------------------------ 
       4.5 |     5.39    |    4.80    |   4.75     |  4.36      |  4.39                       |    4.55    |    4.71
    • Frame-accuarcy seems not consistent with WER. Using the train-data as cv, verify the learning ability of the model. 
   Seems in one nnet model the train top frame accuracy is not consistent with the WER.
    • Decode test_clean_wv1 dataset.
  • AURORA4 dataset 
  (1) Train: train_nosiy
   drop-retention/testcase(WER) | test_clean_wv1  | test_airport_wv1 | test_babble_wv1 | test_car_wv1 
   ---------------------------------------------------------------------------------------------------------
          std-baseline          |  9.60           |  11.41           |  11.63          |  8.64
   ---------------------------------------------------------------------------------------------------------
             dp-0.3             |  12.91          |  16.55           |  15.37          |  12.60
   ---------------------------------------------------------------------------------------------------------
             dp-0.4             |  11.48          |  14.43           |  13.23          |  11.04
   ---------------------------------------------------------------------------------------------------------
             dp-0.5             |  10.53          |  13.00           |  12.89          |  10.24
   ---------------------------------------------------------------------------------------------------------
             dp-0.6             |  10.02          |  12.32           |  11.81          |  9.29
   ---------------------------------------------------------------------------------------------------------
             dp-0.7             |  9.65           |  12.01           |  12.09          |  8.89
   ---------------------------------------------------------------------------------------------------------
             dp-0.8             |  9.79           |  12.01           |  11.77          |  8.91
   ---------------------------------------------------------------------------------------------------------
             dp-1.0             |  9.94           |  11.33           |  12.05          |  8.32
   ---------------------------------------------------------------------------------------------------------
     baseline_dp0.4_lr0.008     |  9.52           |  12.01           |  11.75          |  9.44
  ---------------------------------------------------------------------------------------------------------
     baseline_dp0.4_lr0.0001    |  9.92           |  14.22           |  13.59          |  10.24
  ---------------------------------------------------------------------------------------------------------
     baseline_dp0.4_lr0.00001   |  9.06           |  13.27           |  13.14          |  9.33
  ---------------------------------------------------------------------------------------------------------
     baseline_dp0.8_lr0.008     |  9.16           |  11.23           |  11.42          |  8.49
  ---------------------------------------------------------------------------------------------------------
     baseline_dp0.8_lr0.0001    |  9.22           |  11.52           |  11.77          |  8.82
  ---------------------------------------------------------------------------------------------------------
     baseline_dp0.8_lr0.00001   |  9.12           |  11.27           |  11.65          |  8.68
  ---------------------------------------------------------------------------------------------------------
       dp-0.4_follow-std-lr     |  11.33          |  14.60           |  13.50          |  10.95
  ---------------------------------------------------------------------------------------------------------
       dp-0.8_follow-std-lr     |  9.77           |  12.01           |  11.79          |  8.93
  ---------------------------------------------------------------------------------------------------------
         dp-0.4_4-2048          |  11.69          |  16.13           |  14.24          |  11.98
  ---------------------------------------------------------------------------------------------------------
         dp-0.8_4-2048          |  9.46           |  11.60           |  11.98          |  8.78
  ---------------------------------------------------------------------------------------------------------
    • Test with AURORA4 of 7000 (clean + noisy).
    • Follow the standard DNN training learn-rate to avoid the different learn-rate changing time of various DNN training. Similar performance is obtained.
    • Find and test unknown noise test-data.(+)
    • Have done the droptout on normal trained XEnt NNET , eg wsj(learn-rate:1e-4/1e-5). Seems small learn-rate get the balance of accuracy and train-time.
    • Draft the dropout-DNN weight distribution. (++)
  • Rectification
  • Combine drop out and rectifier.(+)
  • Change the learn-rate in the middle of the training, Modify the train_nnet.sh script(Liu Chao).
  • MaxOut
  • 6min/epoch 
1) AURORA4 -15h
   NOTE: gs==groupsize
 (1) Train: train_clean
        model/testcase(WER)    | test_clean_wv1  | test_airport_wv1 | test_babble_wv1 | test_car_wv1 
   ---------------------------------------------------------------------------------------------------------
          std-baseline         |  6.04           |  29.91           |  27.76          |  16.37
   ---------------------------------------------------------------------------------------------------------
          lr0.008_gs6          |                             - 
   ---------------------------------------------------------------------------------------------------------
         lr0.008_gs10          |                             - 
   ---------------------------------------------------------------------------------------------------------
         lr0.008_gs20          |                             - 
   ---------------------------------------------------------------------------------------------------------
      lr0.008_l1-0.01          |                             - 
   ---------------------------------------------------------------------------------------------------------
       lr0.008_l1-0.001        |                             - 
   ---------------------------------------------------------------------------------------------------------
      lr0.008_l1-0.0001        |                             - 
   ---------------------------------------------------------------------------------------------------------
    lr0.008_l1-0.000001        |                             - 
   ---------------------------------------------------------------------------------------------------------
        lr0.008_l2-0.01        |                             - 
   ---------------------------------------------------------------------------------------------------------
           lr0.006_gs10        |                             - 
   ---------------------------------------------------------------------------------------------------------
           lr0.004_gs10        |                             - 
   ---------------------------------------------------------------------------------------------------------
          lr0.002_gs10         |  6.21           |  28.48           |  27.30          |  16.37
   ---------------------------------------------------------------------------------------------------------
          lr0.001_gs1          |                             -
   ---------------------------------------------------------------------------------------------------------
          lr0.001_gs2          |                             -
   ---------------------------------------------------------------------------------------------------------
          lr0.001_gs4          |                             -
   ---------------------------------------------------------------------------------------------------------
          lr0.001_gs6          |  6.04           |  25.17           |  24.31          |  14.19
   ---------------------------------------------------------------------------------------------------------
          lr0.001_gs8          |  5.85           |  25.72           |  24.35          |  14.28
   ---------------------------------------------------------------------------------------------------------
          lr0.001_gs10         |  6.23           |  27.04           |  25.51          |  14.22
   ---------------------------------------------------------------------------------------------------------
          lr0.001_gs15         |  5.94           |  30.10           |  27.53          |  19.00
   ---------------------------------------------------------------------------------------------------------
          lr0.001_gs20         |  6.32           |  28.10           |  26.47          |  16.98
   ---------------------------------------------------------------------------------------------------------
  • P-norm
  • Convolutive network (+)
  • AURORA 4 
                 |  wer | hid-layers | hid-dim | delta-order | splice | lda-dim | learn-rate	| pooling | TBA
-----------------------------------------------------------------------------------------------------------------------
 cnn_std_baseline| 6.70 |     4      | 1200	|      0      |    4   |   198   |   0.008	|   3     |patch-dim1 7 
-----------------------------------------------------------------------------------------------------------------------
 cnn_std_1000_3  | 6.61 |     4      | 1000	|      0      |    4   |   198   |   0.008	|   3     |patch-dim1 7 
-----------------------------------------------------------------------------------------------------------------------
 cnn_std_1400_3  | 6.61 |     4      | 1400	|      0      |    4   |   198   |   0.008	|   3     |patch-dim1 7 
-----------------------------------------------------------------------------------------------------------------------
 cnn_std_1200_4  | 6.91 |     4      | 1200	|      0      |    4   |   198   |   0.008	|   4     |patch-dim1 6 
-----------------------------------------------------------------------------------------------------------------------
 cnn_std_1200_2  | -    |     4      | 1200	|      0      |    4   |   198   |   0.008	|   2     |patch-dim1 8 
-----------------------------------------------------------------------------------------------------------------------
 cnn_std_1200_3  | 6.66 |     5      | 1200	|      0      |    4   |   198   |   0.008	|   3     |patch-dim1 7 
-----------------------------------------------------------------------------------------------------------------------
  • READ paper

Denoising & Farfield ASR

  • ICASSP paper submitted.
  • HOLD

VAD

  • Frame energy feature extraction, done
  • Harmonics and Teager energy features being investigation
  • Previous results to be organized for a paper

Speech rate training

  • 100h random select from 1000h tec dataset
  • baseline and ROS NNet train done, will decoding soon
  • Seems ROS model is superior to the normal one with faster speech

low resource language AM training

  • HOLD
  • Uyghur language model has been released to JT. Done.

Scoring

  • Timber Comparison on testing

Confidence

  • Reproduce the experiments on fisher dataset.
  • Use the fisher DNN model to decode all-wsj dataset
  • preparing scoring for puqiang data

Speaker ID

  • Preparing GMM-based server.
  • EER ~ 11.2% (GMM-based system)
  • test different number of components; fast i-vector computing

Language ID

  • GMM-based language is ready.
  • Delivered to Jietong

Emotion detection

  • Sinovoice is implementing the server


Text Processing

LM development

Domain specific LM

  • domain lm
  • merger weibo、baiduhi and baiduzhidao lm and test (need result)
  • confirm the size of alpa with xiaomin for business application.(like e-13)
  • get the general test data from miaomin .this test set may get from online.
  • trained a new lm: mobile
  • find the optimal lambda for interpolating following LMs: baidu_hi, mobile, sichuanmobile
  • train some more LMs with Zhenlong
  • keep on training sogou2T lm


  • new dict.
  • Tested the earlier vocabulary on 6000.txt with ppl. 
               old150K      new166K      new150K
   baiduzhidao     394          369          333
   baiduhi         217          190          188
  • Built new 100K,150K,200K vocabulary
  • Had fixed some bugs in sogou dict spider.
  • new toolkit:find method to update the new dict. can get new wordlist from sougou and get word information from baidu.(two week)

tag LM

  • set new test
no "北京" in corpus with tag-lm
method baeline weight0.1 weight0.5 weight1 weight2 weight3
wer 56.58 69.49 62.23 58.03 56.90 -
"北京" 6/10 4/10 4/10 2/10 1/10 0
detail 288 ins, 5075 del, 3178 sub 196 ins, 6016 del, 4278 sub 190 ins, 5870 del, 3334 sub 243 ins, 5294 del, 3223 sub 344 ins, 4558 del, 3687 sub -
  • mix seed lm with big lm, test address-tag on big lm

RNN LM

  • rnn
  • RNNLM=>ALPA make a report
  • test RNNLM on Chinese data from jietong-data
  • check the rnnlm code.
  • lstm+rnn
  • check the lstm-rnnlm code

Word2Vector

W2V based doc classification

  • Initial results variable Bayesian GMM obtained. Performance is not as good as the conventional GMM.
  • Non-linear inter-language transform: English-Spanish-Czch: wv model training done, transform model on investigation
  • SSA-based local linear mapping still on running.
  • k-means classes change to 2.
  • Knowledge vector started
  • format the data
  • yuanbin will continue this work with help of xingchao.
  • Character to word conversion
  • prepare the task: word similarity
  • prepare the dict.
  • Google word vector train
  • some ideal will discuss on weekly report.

Translation

  • v4.0 demo released
  • cut the dict and use new segment-tool

QA

deatil:[1]

Spell mistake

  • retrain the ngram model(caoli)
  • prepare the test and development set(caoli)

improve fuzzy match

  • add Synonyms similarity using MERT-4 method

improve lucene search

  • our vsm method
different result in lucene
method lucene vsm_idf(haiguan) VSM_idf(baidu) vsm_idf(tain) vsm_idf(calculate)
Accary 0.6628 0.6228 0.6197 0.5827 0.5426
  • lucene top
  • top10(82.95%),top20(86.34),top50(90.23%),top100(94.11%),top200(96.18%),top1000(97.31%),top2000(97.87%),top5000(98.75%),top10000(99.06)
  • test the result of top(100,200,1000) in full qa(lucene+fuzzymatch)(caoli)
  • lucene Optimization(liurong)
  • rewrite the method to select the 50 standard question not same template.(liurong)
  • check the word segment for template.(liurong)
  • boost the query keyword using IDF
boost keyword in lucene
method Default idf_train idf_train_norm idf_baidu idf_baidu_norm
Accary 0.66228 0.651629 0.57644 0.647869 0.65288
  • using MERT-4 method to get good value of multi-feature.like IDF,NER,baidu_weight,keyword etc.(liurong this month)

Multi-Scene Recognition

  • add the triples search to QA engine
  • discuss the detail and give a report.(liurong)
  • demo (liurong two week)

.

  • new inter will install SEMPRE
取自“http://cslt.org/mediawiki/index.php?title=2014-11-10&oldid=12391”