“ASR:2015-04-20”版本间的差异
来自cslt Wiki
（→Text Processing） |
（→online learning） |
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| 第74行： | 第74行： | ||
===online learning=== | ===online learning=== | ||
| − | + | * modified the listNet SGD | |
| − | + | ||
===relation classifier=== | ===relation classifier=== | ||
* check the CNN code and contact the author of paper | * check the CNN code and contact the author of paper | ||
2015年4月20日 (一) 04:42的版本
Speech Processing
AM development
Environment
- grid-11 often shut down automatically, too slow computation speed.
- add a server(760)
RNN AM
- details at http://liuc.cslt.org/pages/rnnam.html
- tuning parameters on monophone NN
- run using wsj,MPE
Mic-Array
- investigate alpha parameter in time domian and frquency domain
- ALPHA>=0, using data generated by reverber toolkit
- consider theta
Convolutive network
- HOLD
- CNN + DNN feature fusion
RNN-DAE(Deep based Auto-Encode-RNN)
- HOLD -Zhiyong
- http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=261
Speaker ID
Ivector based ASR
- hold
- http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?step=view_request&cvssid=340
- Ivector dimention is smaller, performance is better
- Augument to hidden layer is better than input layer
- train on wsj(testbase dev93+evl92)
Dark knowledge
- http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=264 --zhiyong
- trial on logit matching faild --mengyuan
- adaptation for chinglish under investigation-mengyuan
- unsupervised training with wsj contributes to aurora4 model--xiangyu
- test large database with amida--xiangyu
bilingual recognition
Text Processing
tag LM
- similar word extension in FST
- will check the formula using Bayes and experiment
- add similarity weight
RNN LM
- rnn
- test the ppl and code the character-lm
- lstm+rnn
- check the lstm-rnnlm code about how to Initialize and update learning rate.(hold)
W2V based document classification
- result about VMF model [1]
- reproduce the
- try CNN model
Translation
- v5.0 demo released
- cut the dict and use new segment-tool
Sparse NN in NLP
- test the drop-out model and the performance gets a little improvement, need some result:
- test the order feature ,need some result:
- large dimension result:http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=lr&step=view_request&cvssid=344
online learning
- modified the listNet SGD
relation classifier
- check the CNN code and contact the author of paper
