“ASR:2015-06-29”版本间的差异
来自cslt Wiki
（以“==Speech Processing == === AM development === ==== Environment ==== *grid-14 does not work --mengyuan *grid-15 runs slowly ==== RNN AM==== *morpheme RNN --zhiyuan...”为内容创建页面） |
（→Speech Processing） |
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| 第3行： | 第3行： | ||
==== Environment ==== | ==== Environment ==== | ||
| − | + | ||
| − | + | ||
==== RNN AM==== | ==== RNN AM==== | ||
*morpheme RNN --zhiyuan | *morpheme RNN --zhiyuan | ||
| − | + | ||
==== Mic-Array ==== | ==== Mic-Array ==== | ||
* hold | * hold | ||
* compute EER with kaldi | * compute EER with kaldi | ||
| + | |||
| + | ====Data selection unsupervised learning | ||
| + | * train using aurora4 --zhiyong | ||
| + | * train using wsj --xuewei | ||
====RNN-DAE(Deep based Auto-Encode-RNN)==== | ====RNN-DAE(Deep based Auto-Encode-RNN)==== | ||
| 第32行： | 第35行： | ||
===Dark knowledge=== | ===Dark knowledge=== | ||
| − | * test random last output layer when train MPE--zhiyuan | + | * test random last output layer when train MPE --zhiyuan |
===language vector=== | ===language vector=== | ||
| − | * hold | + | * hold |
| − | + | ||
==Text Processing== | ==Text Processing== | ||
2015年7月1日 (三) 08:07的版本
Speech Processing
AM development
Environment
RNN AM
- morpheme RNN --zhiyuan
Mic-Array
- hold
- compute EER with kaldi
====Data selection unsupervised learning
- train using aurora4 --zhiyong
- train using wsj --xuewei
RNN-DAE(Deep based Auto-Encode-RNN)
- hold
- deliver to mengyuan
Speaker ID
- DNN-based sid --Lantian
Ivector&Dvector based ASR
- hold --Tian Lan
- Cluster the speakers to speaker-classes, then using the distance or the posterior-probability as the metric
- dark-konowlege using i-vector
- train on wsj(testbase dev93+evl92)
- --hold
Dark knowledge
- test random last output layer when train MPE --zhiyuan
language vector
- hold
Text Processing
RNN LM
- character-lm rnn(hold)
- lstm+rnn
- check the lstm-rnnlm code about how to Initialize and update learning rate.(hold)
W2V based document classification
- APSIPA paper
- CNN adapt to resolve the low resource problem
Pair-wise LM
- draft paper of journal
Order representation
- modify the objective function(hold)
- sup-sampling method to solve the low frequence word(hold)
- journal paper
binary vector
- nips paper
Stochastic ListNet
- done
relation classifier
- done
plan to do
- combine LDA with neural network
