“ASR:2015-04-20”版本间的差异
来自cslt Wiki
(→Sparse NN in NLP) |
(→Dark knowledge) |
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(2位用户的3个中间修订版本未显示) | |||
第4行: | 第4行: | ||
==== Environment ==== | ==== Environment ==== | ||
* grid-11 often shut down automatically, too slow computation speed. | * grid-11 often shut down automatically, too slow computation speed. | ||
− | * | + | * New grid-13 added, using gpu970 |
+ | * To update the wiki enviroment infomation | ||
==== RNN AM==== | ==== RNN AM==== | ||
* details at http://liuc.cslt.org/pages/rnnam.html | * details at http://liuc.cslt.org/pages/rnnam.html | ||
− | * | + | * Test monophone on RNN using dark-knowledge |
* run using wsj,MPE | * run using wsj,MPE | ||
− | |||
==== Mic-Array ==== | ==== Mic-Array ==== | ||
+ | * Change the prediction from fbank to spectrum features | ||
* investigate alpha parameter in time domian and frquency domain | * investigate alpha parameter in time domian and frquency domain | ||
* ALPHA>=0, using data generated by reverber toolkit | * ALPHA>=0, using data generated by reverber toolkit | ||
* consider theta | * consider theta | ||
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− | |||
− | |||
− | |||
− | |||
====RNN-DAE(Deep based Auto-Encode-RNN)==== | ====RNN-DAE(Deep based Auto-Encode-RNN)==== | ||
− | * HOLD -Zhiyong | + | * HOLD --Zhiyong Zhang |
* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=261 | * http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=261 | ||
− | |||
===Speaker ID=== | ===Speaker ID=== | ||
− | :* DNN-based sid --Yiye | + | :* DNN-based sid --Yiye Lin |
:* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=327 | :* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=327 | ||
− | ===Ivector based ASR=== | + | ===Ivector&Dvector based ASR=== |
− | * | + | :* Cluster the speakers to speaker-classes, then using the distance or the posterior-probability as the metric |
+ | :* Direct using the dark-knowledge strategy to do the ivector training. | ||
:* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?step=view_request&cvssid=340 | :* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?step=view_request&cvssid=340 | ||
:* Ivector dimention is smaller, performance is better | :* Ivector dimention is smaller, performance is better | ||
第39行: | 第35行: | ||
===Dark knowledge=== | ===Dark knowledge=== | ||
− | :*http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=264 -- | + | :* Ensemble |
− | + | ::*http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=264 --Zhiyong Zhang | |
− | :* adaptation for chinglish under investigation- | + | :* adaptation for chinglish under investigation --Mengyuan Zhao |
− | :* unsupervised training with wsj contributes to aurora4 model-- | + | ::* Try to improve the chinglish performance extremly |
− | :* test large database with | + | :* unsupervised training with wsj contributes to aurora4 model --Xiangyu Zeng |
+ | ::* test large database with AMIDA | ||
===bilingual recognition=== | ===bilingual recognition=== | ||
− | :* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=359-- | + | :* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=359 --Zhiyuan Tang |
==Text Processing== | ==Text Processing== | ||
第72行: | 第69行: | ||
* test the order feature ,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 | * large dimension result:http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=lr&step=view_request&cvssid=344 | ||
− | :* sparse-nn on 1000 dimension(le-6,0. | + | :* sparse-nn on 1000 dimension(le-6,0.705236) is better than 200 dimension(le-12,0.694678). |
===online learning=== | ===online learning=== |
2015年4月22日 (三) 08:49的最后版本
Speech Processing
AM development
Environment
- grid-11 often shut down automatically, too slow computation speed.
- New grid-13 added, using gpu970
- To update the wiki enviroment infomation
RNN AM
- details at http://liuc.cslt.org/pages/rnnam.html
- Test monophone on RNN using dark-knowledge
- run using wsj,MPE
Mic-Array
- Change the prediction from fbank to spectrum features
- investigate alpha parameter in time domian and frquency domain
- ALPHA>=0, using data generated by reverber toolkit
- consider theta
RNN-DAE(Deep based Auto-Encode-RNN)
- HOLD --Zhiyong Zhang
- http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=261
Speaker ID
Ivector&Dvector based ASR
- Cluster the speakers to speaker-classes, then using the distance or the posterior-probability as the metric
- Direct using the dark-knowledge strategy to do the ivector training.
- 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
- Ensemble
- adaptation for chinglish under investigation --Mengyuan Zhao
- Try to improve the chinglish performance extremly
- unsupervised training with wsj contributes to aurora4 model --Xiangyu Zeng
- test large database with AMIDA
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 norm model [1]
- 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
- sparse-nn on 1000 dimension(le-6,0.705236) is better than 200 dimension(le-12,0.694678).
online learning
- modified the listNet SGD
relation classifier
- check the CNN code and contact the author of paper