“VPR tasks”版本间的差异
来自cslt Wiki
(2位用户的3个中间修订版本未显示) | |||
第19行: | 第19行: | ||
Task 4: gated attention for backend scoring | Task 4: gated attention for backend scoring | ||
− | i: | + | i: programing and small scale test: 6.15-6.22. |
− | ii: | + | ii: further large scale test: 6.23-6.30. |
OUTPUT: More accurate VPR backend. | OUTPUT: More accurate VPR backend. | ||
Task 5: enrollment noisy traning/adaption | Task 5: enrollment noisy traning/adaption | ||
− | i: | + | i: Online noise-adding enrollment: 6.4-6.12 |
− | ii: | + | ii: Offline data-augmentation enrollment: 6.12-6.22 --HOLD |
OUTPUT: More noise-robust VPR backend. | OUTPUT: More noise-robust VPR backend. | ||
+ | |||
+ | Task 6: deep speaker VAD | ||
+ | i: SVM-VAD: 6.11-6.15. | ||
+ | ii: DNN-VAD: 6.11-6.15. | ||
+ | OUTPUT: More accurate VAD. |
2018年6月12日 (二) 02:54的最后版本
Task 1: 16k model noisy training
i: large scale noisy training (7500 pnorm) 4.1-5.1. Done ii: model test: 5.1-5.7. Done. iii: full data training (7500+other dataset+more conditions). See (i). Done. OUTPUT: large reliable 16k model. Done.
Task 2: 8k model noisy training
i: small scale verification: 5.1 -5.7. Done. ii: large scale training: 5.14-6.1. @ Lantian. Done. OUTPUT: large reliable 8k model. Done.
Task 3: Full-info training
i: programing and small test: 4.1-5.1. Done ii: full training for 7500 people: 5.1-6.1. Done. iii: adaptation for Ali dataset: 5.15-6.1. Done. iv: applying to 8k model. Done. OUTPUT: more reliable 16k and 8k model. Done.
Task 4: gated attention for backend scoring
i: programing and small scale test: 6.15-6.22. ii: further large scale test: 6.23-6.30. OUTPUT: More accurate VPR backend.
Task 5: enrollment noisy traning/adaption
i: Online noise-adding enrollment: 6.4-6.12 ii: Offline data-augmentation enrollment: 6.12-6.22 --HOLD OUTPUT: More noise-robust VPR backend.
Task 6: deep speaker VAD
i: SVM-VAD: 6.11-6.15. ii: DNN-VAD: 6.11-6.15. OUTPUT: More accurate VAD.