“2014-10-27”版本间的差异

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第14行： 第14行：
 
     grid-14    |    yes        |   
 
     grid-14    |    yes        |   
 
   ------------------------------------------------------------
 
   ------------------------------------------------------------
−
:* buy 760  
+
:* buy 760-GPU
  
 
==== Sparse DNN ====
 
==== Sparse DNN ====
第39行： 第39行：
 
     -------------------------------------------------------------------------  
 
     -------------------------------------------------------------------------  
 
         4.5 |    5.39    |    4.80    |  4.75    |  4.36      |    4.55   
 
         4.5 |    5.39    |    4.80    |  4.75    |  4.36      |    4.55   
−
:* Frame-accuarcy seems not consistent with WER.
+
:** Frame-accuarcy seems not consistent with WER.
−
:* Using the train-data as cv, verify the learning ability of the model.     
+
:** Using the train-data as cv, verify the learning ability of the model.     
  
 
:* AURORA4 dataset
 
:* AURORA4 dataset
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               dp-1.0            |  9.94          |  11.33          |  12.05          |  8.32
 
               dp-1.0            |  9.94          |  11.33          |  12.05          |  8.32
 
     ---------------------------------------------------------------------------------------------------------
 
     ---------------------------------------------------------------------------------------------------------
−
:* Losing important features, enlarge the hidden-layer dim to 2048.
+
:** Losing important features, enlarge the hidden-layer dim to 2048.
−
:* Follow the standard dnn training learn-rate to avoid the different learn-rate changing time of various DNN training.
+
:** Follow the standard dnn training learn-rate to avoid the different learn-rate changing time of various DNN training.
−
:* Test out of known noise test-data.
+
:** Test out of known noise test-data.
−
:* Continue the droptout on normal trained XEnt NNET , eg wsj(learn-rate:1e-4/1e-5). (++)
+
:** Continue the droptout on normal trained XEnt NNET , eg wsj(learn-rate:1e-4/1e-5). (++)
−
:* Draft the dropout-DNN weight distribution. (++)
+
:** Draft the dropout-DNN weight distribution. (++)
  
 
* Rectification
 
* Rectification
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   |      0        |      |      |        |
 
   |      0        |      |      |        |
  
 +
 +
* sub word unit language model is ready. on testing.
 
====Scoring====
 
====Scoring====
 
* Harmonics program done, experiment to be done.
 
* Harmonics program done, experiment to be done.

2014年10月28日 (二) 02:03的最后版本

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-GPU

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
   ---------------------------------------------------------------------------------------------------------
    • Losing important features, enlarge the hidden-layer dim to 2048.
    • Follow the standard dnn training learn-rate to avoid the different learn-rate changing time of various DNN training.
    • Test out of known noise test-data.
    • Continue the droptout on normal trained XEnt NNET , eg wsj(learn-rate:1e-4/1e-5). (++)
    • Draft the dropout-DNN weight distribution. (++)
  • Rectification
  • Still NAN error, need to debug. 
 1) AURORA4 -15h
 (1) Train: train_clean
     learn-rate/testcase(WER)  | test_clean_wv1  | test_airport_wv1 | test_babble_wv1 | test_car_wv1 
   ---------------------------------------------------------------------------------------------------------
          std-baseline         |  6.04           |  29.91           |  27.76          |  16.37
   ---------------------------------------------------------------------------------------------------------
          lr0.001              |  6.28           |  30.01           |  30.26          |  20.81
   ---------------------------------------------------------------------------------------------------------
          lr0.003              |  6.44           |  32.01           |  32.24          |  17.82
   ---------------------------------------------------------------------------------------------------------
          lr0.005              |  6.47           |  33.49           |  34.75          |  18.15
   ---------------------------------------------------------------------------------------------------------
          lr0.007              |  6.72           |  35.85           |  39.72          |  18.03
   ---------------------------------------------------------------------------------------------------------
        lr-0.001_l1-0.001      |  83.19          |  98.57           |  98.84          |  97.77
   ---------------------------------------------------------------------------------------------------------
        lr-0.001_l1-0.0001     |  7.58           |  32.94           |  34.29          |  23.42
   ---------------------------------------------------------------------------------------------------------
       lr-0.001_l1-0.00001     |  6.21           |  29.15           |  28.24          |  19.50
   ---------------------------------------------------------------------------------------------------------
       lr-0.001_l1-0.000001    |  6.30           |  31.91           |  29.23          |  21.52
   ---------------------------------------------------------------------------------------------------------
  • Change the learn-rate in the middle of the training, Modify the train_nnet.sh script(Liu Chao).
  • Using maximum learning-rate.
  • MaxOut (++)
  • Convolutive network (+)
  • Test more configurations

Denoising & Farfield ASR

  • ICASSP paper submitted.
  • HOLD

VAD

  • Spike detection and removal.
  • 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
  • rearrange the ending point of the detected speech

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(+)
  • Tencent training data done

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        |       |       |        |


  • sub word unit language model is ready. on testing.

Scoring

  • Harmonics program done, experiment to be done.
  • Initial experiment shows more timber data are required

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

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
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