“2020-03-02”版本间的差异

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|Zhiyuan Tang
 
|Zhiyuan Tang
 
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* Repeat glow and asr on WSJ dataset.
 
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* Different noise/domain on test.
 
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*Build the baseline models  
 
*Build the baseline models  
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*Learn to use kaldi
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*Derive model formula
 
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*Optimize the baseline models
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*Optimize model structure
 
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2020年3月2日 (一) 00:06的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • Mostly completed the investigation on normalized scoring and complted the draft article
  • Investigated the DNF with simulation data
  • Keep on investigating the DNF model with simulation data
  • Coin some new approach for SID scoring, motivated by the NL interpertation of PLDA.
Yunqi Cai
  • Finished patent
  • Investigate the total covariance constrain of DNF and showed some results
  • Preparing the weekly paper report
  • More investigation on DNF
Zhiyuan Tang
  • Repeat glow and asr on WSJ dataset.
  • Different noise/domain on test.
Lantian Li
  • Submit DNF patent.
  • Complete DNF repository.
  • Start frame-level DNF-SRE training.
  • Go on DNF-SRE.
Ying Shi
  • verify mel spectrum on DAE and double flow
  • train DAE and Double Flow(different layer and different flow type ) with spectrum feats(6 kinds of noise with random snr).
  • compute SDR and PESQ of DAE and Double Flow
  • verify the result
  • compute fwSNR
  • build more powerfull DAE baseline and flow model
Wenqiang Du
  • Using the add noise、 spec-argument 、attention to improve model performance in different device channel
Haoran Sun
Yue Fan
  • Build the baseline models
  • Derive model formula
  • Optimize model structure
Jiawen Kang
  • Collect cnceleb speaker-birth map.
  • Finish length, age and 11*11 geners experiments.
  • Make some distribution figures for different speakers and geners.
  • Design Cross-channel experiments.
Ruiqi Liu
  • Experiments on CN-Celeb different scenes and different length .
  • Continue training the model.
  • More experiments for different speakers and scenes.
Sitong Cheng
Zhixin Liu
Haolin Chen
  • Double WaveGlow on noisy TIMIT
  • Test performance of Double Glow
  • Optimize model structure
  • Comprehensive evaluation on two models
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