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| （3位用户的3个中间修订版本未显示） |
| 第9行： |
第9行： |
| | * Design/Discussion AI popular science | | * Design/Discussion AI popular science |
| | * Conjecture for minmum loss training | | * Conjecture for minmum loss training |
| − |
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| | * | | * |
| 第20行： |
第19行： |
| | |Lantian Li | | |Lantian Li |
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| − | * | + | * GPU status [https://z1et6d3xtb.feishu.cn/wiki/XGcGwRK5viJmpRkjH9AczIhynCh] |
| | + | * INTERSPEECH 2024 |
| | + | * ASIP-BUPT (CohortTSE, SE-Adapter, SpeakerAug, NeuralScoring) |
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| | * | | * |
| 第97行： |
第98行： |
| | |Xiaolou Li | | |Xiaolou Li |
| | || | | || |
| − | * | + | * Finish INTERSPEECH2024 paper |
| | + | * review code of cnvsrc |
| | + | * Next step: |
| | + | ** Focus on model structure of VSR Benchmark |
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| | * | | * |
| 第135行： |
第139行： |
| | |Wan Lin | | |Wan Lin |
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| − | * | + | * Neural scoring [https://z1et6d3xtb.feishu.cn/docx/TQvWdk8LVo9ONaxQ5Qac9A2Dn3d?from=from_copylink] |
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| | * | | * |
| 第171行： |
第175行： |
| | |Junhui Chen | | |Junhui Chen |
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| − | * | + | * Neural scoring |
| | + | * Interim report |
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| | * | | * |
| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
|
- Interspeech 2024 paper refinement
- Design/Discussion AI popular science
- Conjecture for minmum loss training
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| Lantian Li
|
- GPU status [1]
- INTERSPEECH 2024
- ASIP-BUPT (CohortTSE, SE-Adapter, SpeakerAug, NeuralScoring)
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| Ying Shi
|
- Finish INTERSPEECH paper
- Investigate random order SOT for multi-talker ASR task
- 3-mix 0s offset test condition
- DOM-SOT 20.51
- PIT-SOT 23.26
- random-order SOT 26.20
- group work
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|
|
| Zhenghai You
|
- Weekly report
- Some evaluations about TSE speaker encoder
- Huawei project (Phase 1st)
- Some doubts about the paper due to the latest testing in minimum loss
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- Change the speakerbeam speaker encoder to frequency domain
- Train a SID with a speakerbeam structure
|
|
| Junming Yuan
|
- Finish INTERSPEECH paper
- Make the plan for the large vocabulary pretraining task.
- Focus on the experimental details of the few-shot paper from Google.
- Try to address the 3 questions:
- How to change MT pretraining model structure?
- How to train three strictly comparable pretraining models based on MT, Hubert, and wav2vec?
- Why does Hubert+MT perform significantly better?
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|
|
| Chen Chen
|
- Finish IS24 paper
- Some documents for VTS X project
- Proposal for next stage work on VSR/VTS
- Focus on two task: 1) CNCVS2 dataset 2) Mandarin VSR Benchmark [2] on CNCVS1&2&CNVSRC
- Aim at a solid benchmark with data/code/model
- Perhaps a long journal paper
|
- Conditional entropy analysis of VTS task
- MFA is done
- TODOs: feature/embedding extracting, clustering, discrete conditional entropy calculating
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|
| Xiaolou Li
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- Finish INTERSPEECH2024 paper
- review code of cnvsrc
- Next step:
- Focus on model structure of VSR Benchmark
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|
|
| Zehua Liu
|
- Finish IS24
- VSR work continues
|
|
|
| Pengqi Li
|
- Extending workshop paper
- Finish slide for workshop paper.
- make plan, investigate, prepare dataset for extending paper.
- Rethink how to design a method that can globally PID
- Team Working[3]
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|
| Wan Lin
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| Tianhao Wang
|
- Finish INTERSPEECH paper
- Code reorganization
|
|
|
| Zhenyu Zhou
|
- InterSpeech2024 submission
- Code reorganization
- Neuro scoring reviewing
|
|
|
| Junhui Chen
|
- Neural scoring
- Interim report
|
|
|
| Jiaying Wang
|
- weekly report
- PIT baseline: ConTasNet (finish tonight)
- test whether the separation target is the closer one to the cohort embedding: the rate is around 0.5
- confused about the efficiency of cohort
- Further experiment:TasNet with minimal loss
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|
|
| Yu Zhang
|
- Portfolio backtesting report
- stock trade API
|
|
|
| Wenqiang Du
|
- Aibabel
- Control Uyghur KWS model FA,but not get a good performance yet.
- Continue test and update CN KWS model
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|
|
| Yang Wei
|
- Read training code of Paraformer model, in order to get intermediate data
- Prepare Huilan product training, and deal with problems of ASR and TTS service
|
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|
| Lily
|
- Paper reading
- Prepare for overview paper
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| Turi
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- Data collection app[5]
- Course works
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