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| | |Jiaying Wang | | |Jiaying Wang |
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| − | * | + | * paper reading report |
| | + | * Huawei data collection |
| | + | * cohort test[https://z1et6d3xtb.feishu.cn/docx/IeIydyjzJozfUWxvmNfcWASBngg] |
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| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
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- 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.
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| Lantian Li
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- GPU status [1]
- Projects (AED, TSE)
- ASIP-BUPT (NeuralScoring, CohortSS)
- BlockChain Courses
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| Ying Shi
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| Zhenghai You
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| Junming Yuan
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- AI Graph slides refinement
- IS24 rebuttal
- Control FA experiment baseline result[2]
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| Chen Chen
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- prepare CNVSRC2024 Baseline system
- vii group [3]
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| Xiaolou Li
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- CNVSRC2024 baseline training
- IS24 Rebuttal
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| Zehua Liu
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- AKVSR code
- data crop
- IS24 Rebutall
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| Pengqi Li
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- Read&Summary SA-XAI workshop(ICASSP) papers
- Experiment(PID) on Timit(Extend workshop paper)
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| Wan Lin
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- Pre & QA for graduation paper
- Neural Scoring [4]
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| Tianhao Wang
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- Neural Scoring [5]
- IS24 rebuttal
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| Zhenyu Zhou
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- Paper reading
- IS24 rebuttal
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| Junhui Chen
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- Graduation paper
- 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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| Jiaying Wang
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- paper reading report
- Huawei data collection
- cohort test[6]
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| Yu Zhang
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- AutoML:
- switched from FLAML to EVALML as it provided a tool chain better suited to our tasks
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| Wenqiang Du
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- Hard negative training,FA data is ready,prepare to train the model
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| Yang Wei
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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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| Lily
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| Turi
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- Data collection
- Course work & paper reading
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| Yue Gu
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- Semantic paraformer model reconstruction
- Interspeech rebuttal
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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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