“2020-03-16”版本间的差异

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(6位用户的8个中间修订版本未显示)
第6行: 第6行:
 
|Dong Wang
 
|Dong Wang
 
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* Finish the analysis for optimal score
 +
* Design a new approach for condition transfer
 +
* Initial experiment on subspace DNF
 
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*  
+
* Compte a draft on condition transfer
 +
* More experiments on subspace DNF
 +
* More investigation on distirbutional x-vector
 
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第28行: 第32行:
 
|Zhiyuan Tang
 
|Zhiyuan Tang
 
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*  
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* Conditioned flow with waveglow backend.
 
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* Conditioned flow with waveglow backend.
 
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第39行: 第43行:
 
|Lantian Li
 
|Lantian Li
 
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*  
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* Prepare back-end scoring (python vs. Kaldi).
 +
* Fast decoding of xvector in tf-kaldi.
 +
* Clean up tools (Leven dis and local CMD).
 
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*  
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* Go on back-end scoring.
 
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第50行: 第56行:
 
|Ying Shi
 
|Ying Shi
 
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 +
* Report about speech enhancement
 
* Summarize current conclusions and make plan
 
* Summarize current conclusions and make plan
 
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第98行: 第105行:
 
|Jiawen Kang
 
|Jiawen Kang
 
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*  
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* Learning Meta learning
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* Experiments report
 
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*
+
* Arragement code.
 +
* implement metalearning
 
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第121行: 第130行:
 
|Sitong Cheng
 
|Sitong Cheng
 
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*  
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* DNF of pronunciation scoring.
 
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* Still DNF.
 +
* Do some experiments.
 
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第132行: 第142行:
 
|Zhixin Liu
 
|Zhixin Liu
 
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*  
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* Train baseline
 
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* Analyze result
 
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2020年3月16日 (一) 11:31的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • Finish the analysis for optimal score
  • Design a new approach for condition transfer
  • Initial experiment on subspace DNF
  • Compte a draft on condition transfer
  • More experiments on subspace DNF
  • More investigation on distirbutional x-vector
Yunqi Cai
  • investigate energy model
  • investigate energy model on NL socre
Zhiyuan Tang
  • Conditioned flow with waveglow backend.
  • Conditioned flow with waveglow backend.
Lantian Li
  • Prepare back-end scoring (python vs. Kaldi).
  • Fast decoding of xvector in tf-kaldi.
  • Clean up tools (Leven dis and local CMD).
  • Go on back-end scoring.
Ying Shi
  • Report about speech enhancement
  • Summarize current conclusions and make plan
  • Retrain DAE, double flow and double AE
  • Train Flow-VAE
  • Prepare paper
Wenqiang Du
Haoran Sun
Yue Fan
  • Complete half of the data collection
  • Promotion of crawler tools
  • Simultaneously work on the data download and pipeline process of cn2
  • Train dnf model with gun&nongun and test
Jiawen Kang
  • Learning Meta learning
  • Experiments report
  • Arragement code.
  • implement metalearning
Ruiqi Liu
  • Training model and arranging experiments data.
  • Arranging experiments code.
  • Investigate meta-learning and do experiments
Sitong Cheng
  • DNF of pronunciation scoring.
  • Still DNF.
  • Do some experiments.
Zhixin Liu
  • Train baseline
  • Analyze result
Haolin Chen
  • Train double glow with white-noised TIMIT, mel sprctrogram
  • Replace noisy model in double glow with VAE
  • Full evaluation using PESQ, STOI, fwsegSNR on white-noised test data
  • Summarize result
  • Prepare paper