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| 第10行： |
第10行： |
| | |Miao Zhang | | |Miao Zhang |
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| − | * | + | * Read the 16k model scripts |
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| − | * | + | * The cough recognition codes left by Xiaofei |
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| | * | | * |
| Date |
People |
Last Week |
This Week |
Task Tracking
|
| 2017.12.25
|
| Miao Zhang
|
- Read the 16k model scripts
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- The cough recognition codes left by Xiaofei
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|
| Ying Shi
|
- some function for voice-printer
- speaker vector per utterance here
- speaker vector minus base speaker vector here
- CTC for Haibo Wang (Token accuracy on train set 92.80%, on cv set 89.74%) haven't test on test set
- QRcode
- speaker vector merge phone grayscale here
- speaker vector merge phone black-and-white map here
- speaker vector merge phone black-and-white map minus base vector here
- ivector baseline for kazak-uyghur LRE performance is 81.85% (Utt level)
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- Finish voice-checker copyright and submit the copyright in this Wednesday
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|
| Lantian Li
|
- Complete the recipe for `VV_FACTOR`.
- 16K and 8K deep speaker model comparison.[1]
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- Patent for `VV_QuickMark`.
- Complete the demo for `VV_FACTOR`.[Assign to Shouyi Dai]
- Phonetic speaker embedding.
- Overlap training for speaker features.
|
|
| Zhiyuan Tang
|
- word level pronunciation accuracy based on likelihood (tell which word is well pronounced as '0' or badly pronounced '1')
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- model adaptation
- if possible, an alpha version Parrot for test inside lab to collect some data for better configurature
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| Date |
People |
Last Week |
This Week |
Task Tracking
|
| 2017.12.18
|
| Ying Shi
|
- Finish the Voice-printer program
- Apply the software copyright of Voice-printer
- APSIPA 2017
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- Finish the software copyright of Voice-checker
- Baseline of similar language recongnition system(i-vector, DNN, PTN)
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- focus on function other than UI
- i-vector LID first
|
| Lantian Li
|
- Optimize the demo of `VV_Seg` and `VV_QuickMark`.
- Phone-aware scorning on deep speaker feature. [2]
|
- Phone-aware scorning.
- Overlap training for speaker features.
|
|
| Zhiyuan Tang
|
- easy-to-read interfaces for Parrot
|
- phone-level likelihood for detail diagnosis and an alpha version Parrot for test inside lab
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|
