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| 第30行： |
第30行： |
| | |Lantian Li | | |Lantian Li |
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| − | * | + | * Refine AI course v2. |
| | + | * Check spoof paper. |
| | + | * Finish my defences. |
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| − | * | + | * Finish ETM response. |
| | + | * Exps of hard trials. |
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| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
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- Spoof paper refined
- Start the hard trials paper
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| Yunqi Cai
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- img fusion network construction
- infra experiments plan for interns
- bayesian optimization paper review
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| Lantian Li
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- Refine AI course v2.
- Check spoof paper.
- Finish my defences.
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- Finish ETM response.
- Exps of hard trials.
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| Ying Shi
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- Test fncmd and speech engrave on huawei_cross_channel data here
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- Retrain speech engrave model(make speech engrave and fncmd are Comparable on far field test set)
- Huawei cross channel data
- Score margin
- Discriminative training
- Retrain fncmd model with huawei data.
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| Haoran Sun
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- some analysis on c-vector
- training processing of c-vector
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- remove f0 decoder of c-vector
- a easier model with only content and speaker encoders based on long-short term assumption
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| Chen Chen
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- perform kmeans and pca on wav2vec result
- check GAN
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| Pengqi Li
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- Verifying the correctness of the a series of cam method
- reproduce the method of Layer-CAM on classification
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- more experiment and analysis on this method
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| Weida Liang
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- Finish training for not-ever-seen speaker on baseline AE and cycle model
- Build the framework of wav2vec model
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- Full test on baseline & cycle model
- More details need to be discussed on wav2vec model
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| Zixi Yan
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- Fine-tune the wav2vec model on dev-other
- Test the effect of Tibetan adjusted model
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| Sirui Li
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- Compare the effects of TIMIT and Tibetan fine-tune
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- More comparative experiments
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| Haoyu Jiang
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| Ruihai Hou
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| Renmiao Chen
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- choose thresholds for dividing high-confident data, mid-confident data, low-confident data.
- check the thresholds.
- use speechbrain to do IDR task.
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- do more task with the data.
- finish the report.
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