“2014-10-27”版本间的差异

来自cslt Wiki
跳转至： 导航、 搜索
（→‎QA）
第14行： 第14行：
 
     grid-14    |    yes        |   
 
     grid-14    |    yes        |   
 
   ------------------------------------------------------------
 
   ------------------------------------------------------------
 +
:* buy 760
  
 
==== Sparse DNN ====
 
==== Sparse DNN ====
第19行： 第20行：
 
* Experiments show that  
 
* Experiments show that  
 
* Suggest to use TIMIT / AURORA 4 for training
 
* Suggest to use TIMIT / AURORA 4 for training
 +
* HOLD
  
 
==== RNN AM====
 
==== RNN AM====
−
* Initial test on WSJ , leads to out-memory.
+
* Initial nnet seems no very well, need to be pre-trained or test lower learn-rate.
 +
* For AURORA4 1h/epoch, 100 epochs done.
 
* Using AURORA 4 short-sentence with a smaller number of targets.
 
* Using AURORA 4 short-sentence with a smaller number of targets.
  
 
====Noise training====
 
====Noise training====
−
* First draft of the noisy training journal paper  
+
* First draft of the noisy training journal paper.
 +
* Second version released.
 
* Paper Correction (Yinshi, Liuchao, Lin Yiye), be going.
 
* Paper Correction (Yinshi, Liuchao, Lin Yiye), be going.
  
第32行： 第36行：
 
* Drop out
 
* Drop out
 
:* dataset:wsj, testset:eval92
 
:* dataset:wsj, testset:eval92
−
         std |  dropout0.4 | dropout0.5 | dropout0.7 | dropout0.8
+
         std |  dropout0.4 | dropout0.5 | dropout0.6 | dropout0.7 | dropout0.8
−
     -------------------------------------------------------------  
+
     -------------------------------------------------------------------------  
−
         4.5 |    5.39    |    4.80    |  4.36     |    -    
+
         4.5 |    5.39    |    4.80    |  4.75     |  4.36      |    4.55 
 +
:* Frame-accuarcy seems not consistent with WER.
 +
:* Using the train-data as cv, verify the learning ability of the model.   
  
−
:* Test on noisy AURORA4 dataset
+
:* AURORA4 dataset
−
        std |  dropout0.4 | dropout0.5 | dropout0.7 | dropout0.8
+
  (1) Train: train_clean
−
    -------------------------------------------------------------  
+
     
−
      6.05 |    -       |    -       |   -       |   -
+
    drop-retention/testcase(WER) | test_clean_wv1  | test_airport_wv1 | test_babble_wv1 | test_car_wv1
 +
    ---------------------------------------------------------------------------------------------------------
 +
          std-baseline          |  6.04          |  29.91          |  27.76          |  16.37
 +
    ---------------------------------------------------------------------------------------------------------
 +
      dp-0.4             | 6.61          |  29.59          |  30.12          |  19.40
 +
    ---------------------------------------------------------------------------------------------------------
 +
      dp-0.5             | 6.40          |  28.07          |  27.88          |  19.88
 +
    ---------------------------------------------------------------------------------------------------------
 +
              dp-0.6            |  6.36          |  26.68          |  24.85          |  18.32
 +
    ---------------------------------------------------------------------------------------------------------
 +
      dp-0.7             | 6.13          |  25.53          |  23.90          |  15.69
 +
    ---------------------------------------------------------------------------------------------------------
 +
              dp-0.8             |  5.94          |  24.94          |  23.67          |  15.77
 +
    ---------------------------------------------------------------------------------------------------------
 +
              dp-0.9            | 5.96          |  27.30          |  25.63          |  15.46
 +
     ---------------------------------------------------------------------------------------------------------
 +
 
 +
  (2) 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
 +
    ---------------------------------------------------------------------------------------------------------
 +
:* Enlarge the hidden-layer dim to 2048.
 
:* Continue the droptout on normal trained XEnt NNET , eg wsj. (+)
 
:* Continue the droptout on normal trained XEnt NNET , eg wsj. (+)
 
:* Draft the dropout-DNN weight distribution. (+)
 
:* Draft the dropout-DNN weight distribution. (+)

2014年10月27日 (一) 08:20的版本

Speech Processing

AM development

Contour

  • NAN problem
  • nan recurrence 
  ------------------------------------------------------------
   grid/atr.  |   Reproducible  |    add.
  ------------------------------------------------------------
   grid-10    |     yes         |   
  ------------------------------------------------------------
   grid-12    |     no          | "nan" in different position
  ------------------------------------------------------------
   grid-14    |     yes         |  
  ------------------------------------------------------------
  • buy 760

Sparse DNN

  • Performance improvement found when pruned slightly
  • Experiments show that
  • Suggest to use TIMIT / AURORA 4 for training
  • HOLD

RNN AM

  • Initial nnet seems no very well, need to be pre-trained or test lower learn-rate.
  • For AURORA4 1h/epoch, 100 epochs done.
  • Using AURORA 4 short-sentence with a smaller number of targets.

Noise training

  • First draft of the noisy training journal paper.
  • Second version released.
  • Paper Correction (Yinshi, Liuchao, Lin Yiye), be going.

Drop out & Rectification & convolutive network

  • Drop out
  • dataset:wsj, testset:eval92 
       std |  dropout0.4 | dropout0.5 | dropout0.6 | dropout0.7 | dropout0.8
    ------------------------------------------------------------------------- 
       4.5 |     5.39    |    4.80    |   4.75     |  4.36      |    4.55
  • Frame-accuarcy seems not consistent with WER.
  • Using the train-data as cv, verify the learning ability of the model.
  • AURORA4 dataset 
  (1) Train: train_clean 
     
   drop-retention/testcase(WER) | test_clean_wv1  | test_airport_wv1 | test_babble_wv1 | test_car_wv1 
   ---------------------------------------------------------------------------------------------------------
          std-baseline          |  6.04           |  29.91           |  27.76          |  16.37
   ---------------------------------------------------------------------------------------------------------

dp-0.4 | 6.61 | 29.59 | 30.12 | 19.40 

   ---------------------------------------------------------------------------------------------------------

dp-0.5 | 6.40 | 28.07 | 27.88 | 19.88 

   ---------------------------------------------------------------------------------------------------------
             dp-0.6             |  6.36           |  26.68           |  24.85          |  18.32
   ---------------------------------------------------------------------------------------------------------

dp-0.7 | 6.13 | 25.53 | 23.90 | 15.69 

   ---------------------------------------------------------------------------------------------------------
             dp-0.8             |  5.94           |  24.94           |  23.67          |  15.77
   ---------------------------------------------------------------------------------------------------------
             dp-0.9             |  5.96           |  27.30           |  25.63          |  15.46
   ---------------------------------------------------------------------------------------------------------
 
  (2) 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
   ---------------------------------------------------------------------------------------------------------
  • Enlarge the hidden-layer dim to 2048.
  • Continue the droptout on normal trained XEnt NNET , eg wsj. (+)
  • Draft the dropout-DNN weight distribution. (+)
  • Rectification
  • Still NAN error, need to debug. (+)
  • MaxOut (+)
  • Convolutive network
  • Test more configurations
  • Yiye will work on CNN
  • Reading CNN tutorial

Denoising & Farfield ASR

  • ICASSP paper submitted.

VAD

  • Add more silence tag "#" in pure-silence utterance text(train).
  • xEntropy model be training
  • need to test baseline.
  • Sum all sil-pdf as the silence posterior probability.
  • Program done, to tune the threshold

Speech rate training

  • Seems ROS model is superior to the normal one with faster speech
  • Suggest to extract speech data of different ROS, construct a new test set(+)
  • Suggest to use Tencent training data(+)

low resource language AM training

  • Use Chinese NN as initial NN, change the last layer
  • Various the used Chinese trained DNN layer numbers.
    • feature_transform = 6000h_transform + 6000_N*hidden-layers 
 nnet.init = random (4-N)*hidden-layers + output-layer
 | N / learn_rate | 0.008         | 0.001 | 0.0001 |
 |   baseline     | 17.00(14*2h)  |       |        |
 |       4        | 17.75(9*0.6h) | 18.64 |        |
 |       3        | 16.85         |       |        |
 |       2        | 16.69         |       |        |
 |       1        | 16.87         |       |        |
 |       0        | 16.88         |       |        |
    • feature_transform = uyghur_transform + 6000_N*hidden-layers 
 nnet.init = random (4-N)*hidden-layers + output-layer
 Note: This is reproduced Yinshi's experiment
 | N / learn_rate | 0.008 | 0.001 | 0.0001 |
 |   baseline     | 17.00 |       |        |
 |       4        | 28.23 | 30.72 | 37.32  |
 |       3        | 22.40 |       |        |
 |       2        | 19.76 |       |        |
 |       1        | 17.41 |       |        |
 |       0        |       |       |        |
    • feature_transform = 6000_transform + 6000_N*hidden-layers 
 nnet.init = uyghur (4-N)*hidden-layers + output-layer
 | N / learn_rate | 0.008 | 0.001 | 0.0001 |
 |   baseline     | 17.00 |       |        |
 |       4        | 17.80 | 18.55 | 21.06  |
 |       3        | 16.89 | 17.64 |        |
 |       2        |       |       |        |
 |       1        |       |       |        |
 |       0        |       |       |        |

Scoring

  • global scoring done.
  • Pitch & rhythm done, need testing
  • Harmonics program done, experiment to be done.

Confidence

  • Reproduce the experiments on fisher dataset.
  • Use the fisher DNN model to decode all-wsj dataset


Speaker ID

  • Preparing GMM-based server.

Emotion detection

  • Sinovoice is implementing the server


Text Processing

LM development

Domain specific LM

  • domain lm
  • am:1400h(2.0.b) .result: xiaomi-29.43%,baiduzhidao-43.46%,baiduHi-30.02%, test-set:8ksentence(16k=>8k)
  • need to check the xiaomin-lm method and result.
  • new dict.
  • weibo-data : Tencent-segment and count. get 16k words to segment again.
  • new toolkit:find method to update the new dict. can get new wordlist from sougou and get word information from baidu.

tag LM

  • set new test
  • 1k address from dianxin. prepare to test.
  • insert the new unknown-address to test set.
  • record test set 15-sentence/person on dianxin txt.

RNN LM

  • rnn
  • RNNLM=>ALPA
  • train RNNLM on Chinese data from jietong-data
  • lstm+rnn
  • wer:6.2%(4-epoch).need to check the problem.

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

  • v3.0 demo released
  • still slow
  • re-segment the word using new dictionary.will use the tencent-dic about 11w.
  • check new data.

QA

  • search method:
  • test the lucene method
  • analysis the test result
  • add IDF to test
  • spell check
  • get ngram tool and make a simple demo.
  • get domain word list and pingyin tool from huilan.
  • new inter will install SEMPRE
取自“http://cslt.org/mediawiki/index.php?title=2014-10-27&oldid=12085”