“ASR:2014-12-15”版本间的差异

2014年12月22日 (一) 06:13的最后版本

Speech Processing

AM development

Environment

• Already buy 3 760GPU
• grid-9/12 760GPU crashed again; grid-11 shutdown automatically.
• Change 760gpu card of grid-12 and grid-14(+).
• First down-frequency of gpu760.

Sparse DNN

• Performance improvement found when pruned slightly
• need retraining for unpruned one; training loss
• 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/rnnam.html
• Reading papers

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
• Test on 4000h dataset.

Dropout & Maxout & Convolutive network

• Drop out(+)

• Use different proportion of noise data to investigate the effect of xEnt and mpe and dropout
  • Problem 1) The effect of dropout in different noise proportion;

           2) The effect of MPE in different noise proportion;
           3) The effect of MPE+dropout in different noise proportion.

• • http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?step=view_request&cvssid=261
• Conclusion

 Dropout is effective for minority.

• • Find and test unknown noise test-data.(++)

• MaxOut

• Pretraining based maxout, can't use large learning-rate.

• P-norm

• Need to solve the too small learning-rate problem
  • Add one normalization layer after the pnorm-layer
  • Add L2-norm upper bound

• Convolutive network (+)

DAE(Deep Atuo-Encode)

 (1) train_clean
   drop-retention/testcase(WER)| test_clean_wv1  | test_airport_wv1 | test_babble_wv1 | test_car_wv1 
  ---------------------------------------------------------------------------------------------------------
      std-xEnt-sigmoid-baseline| 6.04            |    29.91         |   27.76         | 16.37
  ---------------------------------------------------------------------------------------------------------
      std+dae_cmvn_noFT_2-1200 | 7.10            |    15.33         |   16.58         | 9.23
  ---------------------------------------------------------------------------------------------------------
   std+dae_cmvn_splice5_2-100  | 8.19            |    15.21         |   15.25         | 9.31
  ---------------------------------------------------------------------------------------------------------

• test on XinWenLianBo music. results on
  • http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhaomy&step=view_request&cvssid=318

Denoising & Farfield ASR

• ICASSP paper submitted.
• HOLD

VAD

• Harmonics and Teager energy features being investigation (++)

Speech rate training

• Data ready on tencent set; some errors on speech rate dependent model. error fixed.
• Retrain new model(+)

Scoring

• Timber Comparison done.
• harmonics based timber comparison: frequency based feature is better. done
• GMM based timber comparison is done. Similar to speaker recognition. done
• TODO: Code checkin and technique report. done

Confidence

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

Speaker ID

• Preparing GMM-based server.
• EER ~ 4% (GMM-based system)--Text independent
• EER ~ 6%(1s) / 0.5%(5s) (GMM-based system)--Text dependent
• test different number of components; fast i-vector computing

• Test with number recordings, The 256 number component is the best.
• Test with text-dependent recordings, The 1024 number component is the best.
• Interpolation alpha is not sensitive.

Language ID

• GMM-based language is ready.
• Delivered to Jietong
• Prepare the test-case

Voice Conversion

• Yiye is reading materials(+)

Text Processing

LM development

Domain specific LM

• domain lm

• Sougou2T : kn-count continue .

• lm v2.0.

• problem that the result is different between kaldi and jitong.

• check the format of data encode in kaldi and jitong.

tag LM

• paper(two week)

• add more related work in introduction and rich the paper

RNN LM

• rnn

• test wer RNNLM on Chinese data from jietong-data(this week)
• generate the ngram model from rnnlm and test the ppl with different size txt.[1]

• lstm+rnn

• check the lstm-rnnlm code about how to Initialize and update learning rate.(hold)

Word2Vector

W2V based doc classification

• Initial results variable Bayesian GMM obtained. Performance is not as good as the conventional GMM.(hold)
• Non-linear inter-language transform: English-Spanish-Czch: wv model training done, transform model on investigation

Knowledge vector

• baseline

• Debug the training part and add normalization, result in cvss[326].

  Category:Mammal orders
    entry number: 15572
    testset: 70 couples (28 data scored 0, 11 data scored 1, 16 data scored 2,15 data scored 3)
                   Spearman rank-order coefficient | Pearson correlation coefficient
Factorization:      0.698714                          0.747226        
Baseline:           0.721214                          0.778838
    testset: 70 couples (7 data scored 0, 2 data scored 1, 5 data scored 2, 8 data scored 3)
Baseline:(category) 0.523433   

• Finish the baseline and compared with simple factorization method based on co-occurrence.
• Visualization of knowledge vector[2]

• test

• set up the test data.

relation

• Accomplish transE with almost the same performance as the paper did(even better)[3]

Character to word

• Character to word conversion(hold)

• prepare the task: word similarity
• prepare the dict.

Translation

• v5.0 demo released

• cut the dict and use new segment-tool

QA

improve fuzzy match

• add Synonyms similarity using MERT-4 method(hold)

improve lucene search

• domain keyword to improve search in lucene using mert

• keyword in sentence,like frequency ,position.
• keyword like pos,parser information.

XiaoI framework

• context in QA

query normalization

• using NER to normalize the word
• new inter will install SEMPRE