“2022-02-21”版本间的差异

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第5行： 第5行：
 
|Dong Wang
 
|Dong Wang
 
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*
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* Experiment on IB control with conditional model [http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=wangd&step=view_request&cvssid=847], rough conclusions were obtained.
 +
* Refine the AV speaker recognition theoretical part.
 +
* Review for ICME.
 
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−
*
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* Complete ICME review
 +
* VQMIVC reproduction, update with random mask
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* Some missing papers treatment: (1) true nonlinear LDA (2) CycleFlow (3) Thermal-visual database
 
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*   
 
*   
第16行： 第20行：
 
|Yunqi Cai
 
|Yunqi Cai
 
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*
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*NSFC Application
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*Materials inverse design investigation
 
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*  
 
*  
第33行： 第38行：
 
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* Submit ASVSpoof response
 
* Submit ASVSpoof response
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* Write C-P map paper
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* Finish Draft of C-P Map paper
 
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*   
 
*   
第42行： 第47行：
 
|Ying Shi
 
|Ying Shi
 
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*
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* Speech engrave on overlap speech data
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* M2ASR final report
 
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−
*
+
* Speech engrave on overlap speech data
 
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*   
 
*   
第53行： 第59行：
 
|Haoran Sun
 
|Haoran Sun
 
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−
*  
+
* autoVC with cycle loss [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/1/16/Autovc-cyc.pdf pdf] [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/6/66/Pre.rar demo]
 
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*  
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* cycle loss after adverserial training
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* VQMIVC
 
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*   
 
*   
第64行： 第71行：
 
|Chen Chen
 
|Chen Chen
 
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−
*
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* Review papers about lip-reading & audio-visual speech recognization
 +
* Prepare data & environment for experiments of AV-Hubert
 
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−
*
+
* <-- keep doing these tasks
 
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*   
 
*   
第75行： 第83行：
 
|Pengqi Li
 
|Pengqi Li
 
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−
*  
+
* collated the visualization methods that have been reproduced
 +
* some scripts for baseline(cncsrc)
 
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−
*  
+
* study feature aggregation
 
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*   
 
*   
第86行： 第95行：
 
|Weida Liang
 
|Weida Liang
 
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−
*
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* Never-before-seen test [http://166.111.134.19:7777/liangwd/paper.html]
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* 3~6 spk cycle loss models on wav2vec+seq2seq model
 +
* Rewrite paper and focus on cycle loss
 
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−
*
+
* Finish paper framework
 +
* Push test on WER scoring
 
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*   
 
*   
第96行： 第108行：
 
|Zixi Yan
 
|Zixi Yan
 
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−
*
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* Multi-language W2V model features were used for ASR experiments and compared with traditional MFCC features
 
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*
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* Asr experiments on different layers of multilingual W2V model
 
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*   
第106行： 第118行：
 
|Sirui Li
 
|Sirui Li
 
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−
*
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* Make an experiment plan
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* Read the HuBERT paper and code
 
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*
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* Finish the hubert-U framework
 
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*   
 
*   
第117行： 第130行：
 
|Haoyu Jiang
 
|Haoyu Jiang
 
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−
*
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*Find the baseline for CN-Celeb speaker identification
 
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*
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*Train this baseline and find face recognition baseline
 
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*   
 
*   
第138行： 第151行：
 
|Renmiao Chen
 
|Renmiao Chen
 
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−
*
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* Check CKA
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* Do experiment for gender
 
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*
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* Do experiment for cross-modal PLDA
 
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2022年2月21日 (一) 11:31的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • Experiment on IB control with conditional model [1], rough conclusions were obtained.
  • Refine the AV speaker recognition theoretical part.
  • Review for ICME.
  • Complete ICME review
  • VQMIVC reproduction, update with random mask
  • Some missing papers treatment: (1) true nonlinear LDA (2) CycleFlow (3) Thermal-visual database
Yunqi Cai
  • NSFC Application
  • Materials inverse design investigation
Lantian Li
  • Push CNCSRC (Data release and SR baseline)
  • Submit Tencent AI Lab project
  • Submit M2ASR concluding report
  • Write ASVSpoof response
  • Submit ASVSpoof response
  • Finish Draft of C-P Map paper
Ying Shi
  • Speech engrave on overlap speech data
  • M2ASR final report
  • Speech engrave on overlap speech data
Haoran Sun
  • cycle loss after adverserial training
  • VQMIVC
Chen Chen
  • Review papers about lip-reading & audio-visual speech recognization
  • Prepare data & environment for experiments of AV-Hubert
  • <-- keep doing these tasks
Pengqi Li
  • collated the visualization methods that have been reproduced
  • some scripts for baseline(cncsrc)
  • study feature aggregation
Weida Liang
  • Never-before-seen test [2]
  • 3~6 spk cycle loss models on wav2vec+seq2seq model
  • Rewrite paper and focus on cycle loss
  • Finish paper framework
  • Push test on WER scoring
Zixi Yan
  • Multi-language W2V model features were used for ASR experiments and compared with traditional MFCC features
  • Asr experiments on different layers of multilingual W2V model
Sirui Li
  • Make an experiment plan
  • Read the HuBERT paper and code
  • Finish the hubert-U framework
Haoyu Jiang
  • Find the baseline for CN-Celeb speaker identification
  • Train this baseline and find face recognition baseline
Ruihai Hou
Renmiao Chen
  • Check CKA
  • Do experiment for gender
  • Do experiment for cross-modal PLDA
取自“http://index.cslt.org/mediawiki/index.php?title=2022-02-21&oldid=37800”