2014-10-27

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2014年10月27日 (一) 09:22Zhangzy（讨论 | 贡献）的版本

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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
   ---------------------------------------------------------------------------------------------------------
  • 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

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
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