“2014-10-27”版本间的差异

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