“ASR:2015-01-05”版本间的差异

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==== Environment ====
 
==== Environment ====
* Modification
 
:* First down-frequency of gpu760
 
:* Improved the gpu Fan-speed
 
:* Change the sleep-mode of gpu
 
 
 
* May gpu760 of grid-14 be something wrong. To be exchanged.
 
* May gpu760 of grid-14 be something wrong. To be exchanged.
* To buy 3*2k PCs.
 
  
 
==== Sparse DNN ====
 
==== Sparse DNN ====
 
* details at http://liuc.cslt.org/pages/sparse.html
 
* details at http://liuc.cslt.org/pages/sparse.html
 +
* need to test clean data
 +
* MPE training to be continue
  
 
==== RNN AM====
 
==== RNN AM====
* Adjusting the learning rate.(+)
+
* Trying toolkit of Microsoft.(+)  
* Trying toolkit of Microsoft.(+)
+
* Trying new LSTM toolkit from Baidu
+
 
* details at http://liuc.cslt.org/pages/rnnam.html
 
* details at http://liuc.cslt.org/pages/rnnam.html
  
==== A new nnet training scheduler ====
+
====Dropout & Maxout & retifier ====
* details at http://liuc.cslt.org/pages/nnet-sched.html
+
* Drop out
* Test 500h dataset, 36-epchs/8-batches --Similar performance observed compared with std recipe
+
:* Change the test data to more noisy data, to verify the effectiveness of dropout.
* Test on 36600h dataset --done.
+
 
+
====Dropout & Maxout & Convolutive network====
+
 
+
* Drop out(+)
+
:** Find and test unknown noise test-data.(++)
+
  
 
* MaxOut && P-norm
 
* MaxOut && P-norm
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:** Add one normalization layer after the pnorm-layer
 
:** Add one normalization layer after the pnorm-layer
 
:** Add L2-norm upper bound
 
:** Add L2-norm upper bound
 +
:* hold
  
 +
====Convolutive network====
 
* Convolutive network(DAE)
 
* Convolutive network(DAE)
 
:* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=311
 
:* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=311
 +
:* Feature extractor
 +
:** Combined with raw features, better performance obsearved.
 +
:** Technical report to draft, Yiye Lin, Shi Yin, Menyuan Zhao and Mian Wang
 
:* To test real enviroment echo.
 
:* To test real enviroment echo.
  
====DAE(Deep Atuo-Encode-DNN)====
+
====DNN-DAE(Deep Atuo-Encode-DNN)====
 
:* test on XinWenLianBo music. results on  
 
:* 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
 
:** http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhaomy&step=view_request&cvssid=318
 +
:** Technical report to draft, Mengyuan Zhao and Zhiyong Zhang.
 
:* To test real enviroment echo.
 
:* To test real enviroment echo.
  
 
====VAD====
 
====VAD====
* Harmonics and Teager energy features being investigation (++)
+
* Harmonics and Teager energy features done.
 +
* Model to be trained.
  
 
====Speech rate training====
 
====Speech rate training====
 
:* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=268
 
:* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=268
 +
:* Technical report to draft. Shi Yin
  
 
====Confidence====
 
====Confidence====
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* HOLD
 
* HOLD
  
===Speaker ID===
+
====Neural network visulization====
:* Non-stream GMM:wer-2.28%
+
:* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=324
  seperate3-ivector:wer-3.54 single-ivector:wer-1.57  
+
 
  seperate-PLDA:wer-0.87 single-PLDA:wer-1.00 
+
===Speaker ID===  
:* Code ready
+
:* DNN-based sid
 +
:* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=327
  
 
===Language ID===
 
===Language ID===
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* Prepare the test-case
 
* Prepare the test-case
 
* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=328
 
* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=328
* To test 10 language-ids
 
  
 
===Voice Conversion===
 
===Voice Conversion===

2015年1月9日 (五) 10:05的最后版本

Speech Processing

AM development

Environment

  • May gpu760 of grid-14 be something wrong. To be exchanged.

Sparse DNN

RNN AM

Dropout & Maxout & retifier

  • Drop out
  • Change the test data to more noisy data, to verify the effectiveness of dropout.
  • MaxOut && P-norm
  • Need to solve the too small learning-rate problem
    • Add one normalization layer after the pnorm-layer
    • Add L2-norm upper bound
  • hold

Convolutive network

  • Convolutive network(DAE)

DNN-DAE(Deep Atuo-Encode-DNN)

VAD

  • Harmonics and Teager energy features done.
  • Model to be trained.

Speech rate training

Confidence

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

Neural network visulization

Speaker ID

Language ID

Voice Conversion

  • Yiye is reading materials(+)


Text Processing

LM development

Domain specific LM

  • LM2.0
  • mix the sougou2T-lm,kn-discount continue
  • train a large lm using 25w-dict.(hanzhenglong/wxx)
  • prun history lm(wxx)
  • new dict.
  • dongxu help zhenglong with large dictionary.

tag LM

  • need to do
  • tag Probability should test add the weight(hanzhenglong) and handover to hanzhenglong (hold)
paper
  • paper submit this week.

RNN LM

  • rnn
  • test wer RNNLM on Chinese data from jietong-data
  • generate the ngram model from rnnlm and test the ppl with different size txt.
  • 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

  • Knowledge vector
  • Make a proper test set.
  • Modify the object function and training process.

relation

Character to word

  • Character to word conversion(hold)

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

  • add more feature to improve search.
  • POS, NER ,tf ,idf ..

XiaoI framework

  • context in xiaoI
  • make a report

query normalization

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