“2024-04-29”版本间的差异

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（16位用户的18个中间修订版本未显示）
第6行： 第6行：
 
|Dong Wang
 
|Dong Wang
 
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* Presentation for several public AI promotion
 +
* Primary AI, Grade 3 (1), 8 chapters done
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* Some review on linguistic literature about perception uncertainty in pronunciation assessment
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* New form of text enrollment and speech fine-tuning to account for accent-based KWS.
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第17行： 第21行：
 
|Lantian Li
 
|Lantian Li
 
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* GPU status [https://z1et6d3xtb.feishu.cn/wiki/XGcGwRK5viJmpRkjH9AczIhynCh]
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* Projects (AED, TSE)
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* ASIP-BUPT (NeuralScoring, CohortSS)
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* BlockChain Courses
 
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第28行： 第35行：
 
|Ying Shi
 
|Ying Shi
 
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* DI-TING structure verify [https://z1et6d3xtb.feishu.cn/docx/QldZdNqGdoW8QDxTdFnc0L6Qntc?from=from_copylink here]
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第62行： 第69行：
 
|Chen Chen
 
|Chen Chen
 
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* prepare CNVSRC2024 Baseline system
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* vii group [https://z1et6d3xtb.feishu.cn/docx/EFOzdvhjwohMqLx4Kpice4frnjg?from=from_copylink]
 
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* Rebuttal
 
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第73行： 第81行：
 
|Xiaolou Li
 
|Xiaolou Li
 
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* CNVSRC2024 baseline training
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* IS24 Rebuttal
 
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第84行： 第93行：
 
|Zehua Liu
 
|Zehua Liu
 
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*AKVSR code
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*data crop
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*IS24 Rebutall
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第107行： 第118行：
 
|Wan Lin
 
|Wan Lin
 
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* Pre & QA for graduation paper
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* Neural Scoring [https://z1et6d3xtb.feishu.cn/docx/BywjdkGvNou12sxQ4dAcxYa9noh?from=from_copylink]
 
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第118行： 第130行：
 
|Tianhao Wang
 
|Tianhao Wang
 
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* Neural Scoring [https://z1et6d3xtb.feishu.cn/docx/BywjdkGvNou12sxQ4dAcxYa9noh]
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* IS24 rebuttal
 
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第141行： 第154行：
 
|Junhui Chen
 
|Junhui Chen
 
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* Graduation paper
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* Neural Scoring: chunk 2s->4s, NS is better than EA-ASP
 
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* Try to use large pretrain model as test utt encoder(wavLM, wav2vec2, etc.)
 
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第152行： 第166行：
 
|Jiaying Wang
 
|Jiaying Wang
 
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* paper reading report
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* Huawei data collection
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* cohort test[https://z1et6d3xtb.feishu.cn/docx/IeIydyjzJozfUWxvmNfcWASBngg]
 
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第163行： 第179行：
 
|Yu Zhang
 
|Yu Zhang
 
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* AutoML:
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** switched from FLAML to EVALML as it provided a tool chain better suited to our tasks
 
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第174行： 第191行：
 
|Wenqiang Du
 
|Wenqiang Du
 
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* Hard negative training,FA data is ready,prepare to train the model
 
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第185行： 第203行：
 
|Yang Wei
 
|Yang Wei
 
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*  
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* Children mispronunciation detection and diagnosis
 +
** Prepare baseline recipe and challenge document
 +
** Check the baseline model, due to the extraordinary CER performance.
 
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*
第198行： 第218行：
 
* AIgraph slides
 
* AIgraph slides
 
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第206行： 第226行：
 
|Turi
 
|Turi
 
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* Data collection
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** 14K so far
 +
* Course work & paper reading
 
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第214行： 第236行：
 
|Yue Gu
 
|Yue Gu
 
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* Semantic paraformer model reconstruction
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* Interspeech rebuttal
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*
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* 
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|-
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|Qi Qu
 +
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 +
* Performance test:
 +
** Two-phased KWS vs KWS + FunASR
 +
* Data processing:
 +
** ~100k FA segments collected out of ~6k hours
 +
** Data processing routine
 +
* Model training:
 +
** Fine-tuning w/ ~20k more FA
 
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2024年4月29日 (一) 10:59的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • Presentation for several public AI promotion
  • Primary AI, Grade 3 (1), 8 chapters done
  • Some review on linguistic literature about perception uncertainty in pronunciation assessment
  • New form of text enrollment and speech fine-tuning to account for accent-based KWS.
Lantian Li
  • GPU status [1]
  • Projects (AED, TSE)
  • ASIP-BUPT (NeuralScoring, CohortSS)
  • BlockChain Courses
Ying Shi
  • DI-TING structure verify here
Zhenghai You
Junming Yuan
  • AI Graph slides refinement
  • IS24 rebuttal
  • Control FA experiment baseline result[2]
Chen Chen
  • prepare CNVSRC2024 Baseline system
  • vii group [3]
  • Rebuttal
Xiaolou Li
  • CNVSRC2024 baseline training
  • IS24 Rebuttal
Zehua Liu
  • AKVSR code
  • data crop
  • IS24 Rebutall
Pengqi Li
  • Leave of Absence
  • Read&Summary SA-XAI workshop(ICASSP) papers
  • Experiment(PID) on Timit(Extend workshop paper)
Wan Lin
  • Pre & QA for graduation paper
  • Neural Scoring [4]
Tianhao Wang
  • Neural Scoring [5]
  • IS24 rebuttal
Zhenyu Zhou
  • Paper reading
  • IS24 rebuttal
Junhui Chen
  • Graduation paper
  • Neural Scoring: chunk 2s->4s, NS is better than EA-ASP
  • Try to use large pretrain model as test utt encoder(wavLM, wav2vec2, etc.)
Jiaying Wang
  • paper reading report
  • Huawei data collection
  • cohort test[6]
Yu Zhang
  • AutoML:
    • switched from FLAML to EVALML as it provided a tool chain better suited to our tasks
Wenqiang Du
  • Hard negative training,FA data is ready,prepare to train the model
Yang Wei
  • Children mispronunciation detection and diagnosis
    • Prepare baseline recipe and challenge document
    • Check the baseline model, due to the extraordinary CER performance.
Lily
  • thesis
  • AIgraph slides
Turi
  • Data collection
    • 14K so far
  • Course work & paper reading
Yue Gu
  • Semantic paraformer model reconstruction
  • Interspeech rebuttal
Qi Qu
  • Performance test:
    • Two-phased KWS vs KWS + FunASR
  • Data processing:
    • ~100k FA segments collected out of ~6k hours
    • Data processing routine
  • Model training:
    • Fine-tuning w/ ~20k more FA
取自“http://index.cslt.org/mediawiki/index.php?title=2024-04-29&oldid=41712”