|
|
| （4位用户的8个中间修订版本未显示） |
| 第9行： |
第9行： |
| | * read a paper about driving distraction detection task | | * read a paper about driving distraction detection task |
| | || | | || |
| − | * show normal/distraction patterns of a driver with one class and/or two class SVM | + | * show normal/distraction patterns of a driver with one class and two class SVM |
| | |- | | |- |
| | | | |
| 第18行： |
第18行： |
| | |Hang Luo | | |Hang Luo |
| | || | | || |
| − | * | + | * Compared mono-language model and bi-language model decode result. |
| | + | * Read paper of WFST. |
| | || | | || |
| − | * | + | * Use different corpus or generate mix-lingual corpus to run experiments |
| | |- | | |- |
| | | | |
| 第29行： |
第30行： |
| | * work from Chao Xing down | | * work from Chao Xing down |
| | * kazak lm | | * kazak lm |
| − | ** got some corpus from a student who study in Minzu University of China.But the corpus is short (about 10000) so the ppl is also poor.
| + | ** got some corpus from a student who study in Minzu University of China.But the corpus is short (about 10000) so the ppl is also poor. |
| − | ** spider
| + | ** spider |
| | || | | || |
| − | * | + | * kazak lm |
| | |- | | |- |
| | | | |
| 第40行： |
第41行： |
| | |Yixiang Chen | | |Yixiang Chen |
| | || | | || |
| − | * | + | * Complete the replay task experiment and report'''[delivery]''' |
| | || | | || |
| − | * | + | * learning tensorflow coding DNN and CNN net |
| | |- | | |- |
| | | | |
| 第49行： |
第50行： |
| | |Lantian Li | | |Lantian Li |
| | || | | || |
| − | * | + | * interim report done; |
| | + | * PPT for APSIPA16; |
| | + | * LRE challenge on AP16-OL7. |
| | + | * Deep speaker embedding restart! |
| | + | * Submit TRP-20160011 on Replay detection'''[delivery]'''. |
| | || | | || |
| − | * | + | * Jeju for APSIPA16. |
| | |- | | |- |
| | | | |
| Date |
People |
Last Week |
This Week
|
| 2016.12.12
|
Yanqing Wang
|
- read a paper about driving distraction detection task
|
- show normal/distraction patterns of a driver with one class and two class SVM
|
| Hang Luo
|
- Compared mono-language model and bi-language model decode result.
- Read paper of WFST.
|
- Use different corpus or generate mix-lingual corpus to run experiments
|
| Ying Shi
|
- work from Chao Xing down
- kazak lm
- got some corpus from a student who study in Minzu University of China.But the corpus is short (about 10000) so the ppl is also poor.
- spider
|
|
| Yixiang Chen
|
- Complete the replay task experiment and report[delivery]
|
- learning tensorflow coding DNN and CNN net
|
| Lantian Li
|
- interim report done;
- PPT for APSIPA16;
- LRE challenge on AP16-OL7.
- Deep speaker embedding restart!
- Submit TRP-20160011 on Replay detection[delivery].
|
|
| Zhiyuan Tang
|
- interim report done;
- PPT for APSIPA16;
- language mask[1]
|
|
| Date |
People |
Last Week |
This Week
|
| 2016.11.28
|
Yanqing Wang
|
- finish building GUI of one-class-SVM in Visual Studio
- learn about deep learning
|
- process data with the GUI of one-class-SVM
|
| Hang Luo
|
- Experiments about fixed language model, find language information almost has no improve in ASR so far
- share one paper
- Experiments on smaller language model,128 cells still has great result
|
- Try different language model or other direction
- Paper about joint training
|
| Ying Shi
|
- some work about kazak LM
- DNN for Chao Xing
- ml-book check to cvs
- share two papers
|
- some work about kazak lm(try to reduce the ppl)
- paper about activation function
- DNN for Chao Xing
|
| Yixiang Chen
|
- According to the weighted filter replay test
- fitting warping function and use piecewise function for warping
|
- complete replay test
- learning tensorfloat
|
| Lantian Li
|
- LRE on AP16-OL7. [2]
- The deep model still can not beat the i-vector model.
- Emotion challenge in CCPR. --> Done.
- Replay detection. --> Done.
- Phd forum. --> Done
|
- LRE task.
- Write replay detection technical report.
- oral PPT for APSIPA 2016.
|
| Zhiyuan Tang
|
- materials for PhD Dissertations,
- materials for paper accepted by TASLP;
- language mask[3]
|
- scans the TASLP paper for several times,
- oral PPT for APSIPA 2016.
|
