“ASR:2015-01-05”版本间的差异

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==Speech Processing ==
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=== AM development ===
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==== Environment ====
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* Modification
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:* First down-frequency of gpu760
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:* Improved the gpu Fan-speed
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:* Change the sleep-mode of gpu
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* May gpu760 of grid-14 be something wrong. To be exchanged.
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* To buy 3*2k PCs.
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==== Sparse DNN ====
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* details at http://liuc.cslt.org/pages/sparse.html
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==== RNN AM====
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* Adjusting the learning rate.(+)
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* Trying toolkit of Microsoft.(+)
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* Trying new LSTM toolkit from Baidu
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* details at http://liuc.cslt.org/pages/rnnam.html
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==== A new nnet training scheduler ====
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* details at http://liuc.cslt.org/pages/nnet-sched.html
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* Test 500h dataset, 36-epchs/8-batches --Similar performance observed compared with std recipe
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* Test on 36600h dataset --done.
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====Dropout & Maxout & Convolutive network====
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* Drop out(+)
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:** Find and test unknown noise test-data.(++)
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* MaxOut && P-norm
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:* Need to solve the too small learning-rate problem
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:** Add one normalization layer after the pnorm-layer
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:** Add L2-norm upper bound
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* Convolutive network(DAE)
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:* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=311
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:* To test real enviroment echo.
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====DAE(Deep Atuo-Encode-DNN)====
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:* test on XinWenLianBo music. results on
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:** http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhaomy&step=view_request&cvssid=318
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:* To test real enviroment echo.
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====VAD====
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* Harmonics and Teager energy features being investigation (++)
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====Speech rate training====
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:* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=268
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====Confidence====
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* Reproduce the experiments on fisher dataset.
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* Use the fisher DNN model to decode all-wsj dataset
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* preparing scoring for puqiang data
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* HOLD
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===Speaker ID===
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:* Non-stream GMM:wer-2.28%
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  seperate3-ivector:wer-3.54 single-ivector:wer-1.57 
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  seperate-PLDA:wer-0.87 single-PLDA:wer-1.00 
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:* Code ready
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===Language ID===
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* GMM-based language is ready.
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* Delivered to Jietong
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* Prepare the test-case
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* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=328
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* To test 10 language-ids
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===Voice Conversion===
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* Yiye is reading materials(+)
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==Text Processing==
 
==Text Processing==
 
===LM development===
 
===LM development===

2015年1月9日 (五) 08:17的版本

Speech Processing

AM development

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.
  • To buy 3*2k PCs.

Sparse DNN

RNN AM

A new nnet training scheduler

Dropout & Maxout & Convolutive network

  • Drop out(+)
    • Find and test unknown noise test-data.(++)
  • 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
  • Convolutive network(DAE)

DAE(Deep Atuo-Encode-DNN)

VAD

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

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

Speaker ID

  • Non-stream GMM:wer-2.28% 
  seperate3-ivector:wer-3.54 single-ivector:wer-1.57  
  seperate-PLDA:wer-0.87 single-PLDA:wer-1.00
  • Code ready

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
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