|
|
| （相同用户的3个中间修订版本未显示） |
| 第65行： |
第65行： |
| | * Prepare the input of speech data (trick of block segmentation) | | * Prepare the input of speech data (trick of block segmentation) |
| | * Complete the init version on max-margin SRE. | | * Complete the init version on max-margin SRE. |
| − | * Write TRP-20160012 "基于Kaldi i-vector的说话人识别系统使用说明". | + | * Write TRP-20160012 "基于Kaldi i-vector的说话人识别系统使用说明"'''[delivery]'''. |
| | || | | || |
| | * Prepare the thesis proposal. | | * Prepare the thesis proposal. |
| 第77行： |
第77行： |
| | * Deep speaker embedding | | * Deep speaker embedding |
| | ** Prepare two datasets and make the i-vector baselines. | | ** Prepare two datasets and make the i-vector baselines. |
| − | * Write TRP-20160012 "基于Kaldi i-vector的说话人识别系统使用说明". | + | * Write TRP-20160012 "基于Kaldi i-vector的说话人识别系统使用说明"'''[delivery]'''. |
| | * Write book of robustness SRE. | | * Write book of robustness SRE. |
| | * Wechat open account. | | * Wechat open account. |
| 第91行： |
第91行： |
| | || | | || |
| | * TRP of "How to Config Kaldi nnet3 (in Chinese)", not finished yet; | | * TRP of "How to Config Kaldi nnet3 (in Chinese)", not finished yet; |
| − | * outline of TRP for "Multi-task Recurrent Model for True Multilingual Speech Recognition". | + | * outline of TRP for "Multi-task Recurrent Model for True Multilingual Speech Recognition"; |
| | + | * Generative models, part of Chapter Deep Learning. |
| | || | | || |
| − | * Finish the above 2 TRPs. | + | * Finish the above 3 writings. |
| | |- | | |- |
| | | | |
| Date |
People |
Last Week |
This Week
|
| 2016.12.26
|
Jingyi Lin
|
- Learn and make Dr.Wang's personal web page.
- Prepare for the CSLT's Annual Meeting.
|
- Finish Dr.Wang's personal web page.
- Take photos for menmbers in CSLT.
|
| Yanqing Wang
|
- implement the detection mechanism by socket
- find best parameters to avoid over-fitting
- add two-class-SVM to the program
- make GUI more pretty and easy to use
- improve the program's robustness
- screenshot:
|
- write a document on the program
|
| Hang Luo
|
- Run joint training and write systemic script and documents
|
- Finish joint training documents
- Conclude joint training experiments result
- Make a review on mixlingual
|
| Ying Shi
|
- crawl corpus from internet.(I don't know whether the corpus is right or not.......)
- make new LM(complete)
- train new AM(complete)
- a part of TRP
|
|
| Yixiang Chen
|
- Prepare the input of speech data (trick of block segmentation)
- Complete the init version on max-margin SRE.
- Write TRP-20160012 "基于Kaldi i-vector的说话人识别系统使用说明"[delivery].
|
- Prepare the thesis proposal.
- Integrate CNN + max-margin.
|
| Lantian Li
|
- Deep speaker embedding
- Prepare two datasets and make the i-vector baselines.
- Write TRP-20160012 "基于Kaldi i-vector的说话人识别系统使用说明"[delivery].
- Write book of robustness SRE.
- Wechat open account.
|
- Deep speaker embedding.
- Write book.
- Replay detection on INTERSPEECH chanllenge.
|
| Zhiyuan Tang
|
- TRP of "How to Config Kaldi nnet3 (in Chinese)", not finished yet;
- outline of TRP for "Multi-task Recurrent Model for True Multilingual Speech Recognition";
- Generative models, part of Chapter Deep Learning.
|
- Finish the above 3 writings.
|
| Date |
People |
Last Week |
This Week
|
| 2016.12.19
|
Jingyi Lin
|
|
- Concentrate on checking the cslt.book.
- Prepare for the annual convention.
|
| Yanqing Wang
|
- build a data sender ( read & generate txt files of distracted feature )
- build a data analyzer ( detect the modification of files and make response ( show tokens ) )
- screenshot:
|
- (maybe) replace the detection mechanism by socket
- find best parameters to avoid over-fitting
- add two-class SVM to the program
- make GUI more pretty and easy to use
|
| Hang Luo
|
- Compare decode result between mono and bi LM, and the decode result ues bi LM before and after joint
- Choose wrong decode sentence and find its difference between baseline and shareGMM baseline
- Finished ML book
|
- Continue joint training analysis work, but I'm very confused about how to improve
|
| Ying Shi
|
- some work about kazak lm
- crawl data from kazak internet
|
- run new AM by current speech data
- get more corpus from internet
- use current corpus make LM and decode
|
| Yixiang Chen
|
- Leanring tensorflow
- coding pair wise net use tensorflow
- alter CNN
|
- coding CNN connect pair wise
- Dealing with the issue of different lengths of voice
|
| Lantian Li
|
- LRE challenge on AP16-OL7.
- Jeju for APSIPA16.
|
- LRE on AP16-OL7.
- Deep speaker embedding.
|
| Zhiyuan Tang
|
|
- A speech about recent ASR improvements.
- A supplementary TRP for "Multi-task Recurrent Model for True Multilingual Speech Recognition".
|
