“ASR:2015-06-15”版本间的差异
来自cslt Wiki
（→Speech Processing） |
（→Speech Processing） |
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==== Environment ==== | ==== Environment ==== | ||
| − | * | + | *grid-14 does not work --mengyuan |
| + | *grid-15 runs slowly | ||
| + | |||
==== RNN AM==== | ==== RNN AM==== | ||
| − | *morpheme RNN-zhiyuan | + | *morpheme RNN --zhiyuan |
*RNN MPE --zhiyuan and xuewei | *RNN MPE --zhiyuan and xuewei | ||
==== Mic-Array ==== | ==== Mic-Array ==== | ||
* hold | * hold | ||
| − | |||
| − | |||
| − | |||
| − | |||
* compute EER with kaldi | * compute EER with kaldi | ||
| 第24行： | 第22行： | ||
* DNN-based sid --Lantian | * DNN-based sid --Lantian | ||
:* 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&Dvector based ASR=== | ===Ivector&Dvector based ASR=== | ||
| 第35行： | 第34行： | ||
* test random last output layer when train MPE--zhiyuan | * test random last output layer when train MPE--zhiyuan | ||
| − | |||
| − | |||
| − | |||
===language vector=== | ===language vector=== | ||
* hold --xuewei | * hold --xuewei | ||
| − | + | * train using chinese and chiglish | |
| − | + | ||
| − | * | + | |
==Text Processing== | ==Text Processing== | ||
2015年6月25日 (四) 02:46的最后版本
Speech Processing
AM development
Environment
- grid-14 does not work --mengyuan
- grid-15 runs slowly
RNN AM
- morpheme RNN --zhiyuan
- RNN MPE --zhiyuan and xuewei
Mic-Array
- hold
- compute EER with kaldi
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 --xuewei
- train using chinese and chiglish
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
