“2020-03-09”版本间的差异

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第19行: 第19行:
 
|Yunqi Cai
 
|Yunqi Cai
 
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* Reading papers about SRE back-end score method and Energy based models
 
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* Run experiment on EBMs
 
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第30行: 第30行:
 
|Zhiyuan Tang
 
|Zhiyuan Tang
 
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* Basic glow for speech normalization.
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* Multi-conditioned glow, on phones first.
 
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* Conditioned glow.
 
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第41行: 第42行:
 
|Lantian Li
 
|Lantian Li
 
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* Basic result on subspace DNF.
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* Study on NL scoring.
 
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* NL scoring.
 
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第52行: 第54行:
 
|Ying Shi
 
|Ying Shi
 
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* Double AutoEncoder baseline
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* compute fwSNR
 
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* compare fwSNR between Double AutoEncoder Double Flow  and DAE
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* experiment about semi-supervise Double Flow
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* verify the denoise result via ASR system
 
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第111行: 第116行:
 
|Ruiqi Liu
 
|Ruiqi Liu
 
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* More experiments for different speakers and scenes.
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* Continue training the model.
 
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* Learn Mate-learning,Domain adaptation.
 
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第140行: 第146行:
 
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|Haolin Chen
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* Optimize double glow model structure
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* Calc metrics
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* Verify denoising for ASR
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2020年3月9日 (一) 00:05的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • Almost finished the simulation test for NL
  • Completed proof of several important properties of NL
  • Complete the draft of NL paper
  • More investigation on subspace DNF
Yunqi Cai
  • Reading papers about SRE back-end score method and Energy based models
  • Run experiment on EBMs
Zhiyuan Tang
  • Basic glow for speech normalization.
  • Multi-conditioned glow, on phones first.
  • Conditioned glow.
Lantian Li
  • Basic result on subspace DNF.
  • Study on NL scoring.
  • NL scoring.
Ying Shi
  • Double AutoEncoder baseline
  • compute fwSNR
  • compare fwSNR between Double AutoEncoder Double Flow and DAE
  • experiment about semi-supervise Double Flow
  • verify the denoise result via ASR system
Wenqiang Du
  • Prepare materials for graduation
Haoran Sun
Yue Fan
  • Optimize baseline model
  • Supplement glow experiment
  • Test the crawler code
  • Do experiments on dnf
  • Complete CN2‘s data download
Jiawen Kang
  • Investigation on adaptation.
  • Arranging experiment results.
  • Learning mate-learning.
Ruiqi Liu
  • More experiments for different speakers and scenes.
  • Continue training the model.
  • Learn Mate-learning,Domain adaptation.
Sitong Cheng
Zhixin Liu
Haolin Chen
  • Optimize double glow model structure
  • Calc metrics
  • Verify denoising for ASR