ASR:2015-05-04 - 版本历史

Lr：/* Text Processing */

2015-05-11T00:49:48Z

‎Text Processing

Zxw：/* Speech Processing */

2015-05-06T08:02:01Z

‎Speech Processing

Lr：以“==Speech Processing == === AM development === ==== Environment ==== * grid-11 often shut down automatically, too slow computation speed. * New grid-13 added, using...”为内容创建页面

2015-05-04T01:36:03Z

以“==Speech Processing == === AM development === ==== Environment ==== * grid-11 often shut down automatically, too slow computation speed. * New grid-13 added, using...”为内容创建页面

新页面

==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===
:* DNN-based sid --Yiye Lin
:* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=327

===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
::*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 --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===
:* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=359 --Zhiyuan Tang

==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 [http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=lr&step=view_request&cvssid=355]
* 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

@@ 第48行： / 第48行： @@
 ==Text Processing==
 ====RNN LM====
 *rnn
-:* test the ppl and code the character-lm
+:* test the ppl and code the character-lm(hold)
 *lstm+rnn
 :* check the lstm-rnnlm code about how to Initialize and update learning rate.(hold)
 ====W2V based document classification====
-* result about norm model [http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=lr&step=view_request&cvssid=355]
+* make a technical report about document classification using CNN --yiqiao
-* try CNN model
+* CNN adapt to resolve the low resource problem
-−
 ===Translation===
-* v5.0 demo released
+* similar-pair method in English word using translation model.
-:* 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
+* using sampling method
 ===relation classifier===
-* check the CNN code and contact the author of paper
+* majority of error in others class

@@ 第3行： / 第3行： @@
 ==== Environment ====
-* grid-11 often shut down automatically, too slow computation speed.
+* grid-15 often does not work
-* 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
+* Test monophone on RNN using dark-knowledge --Chao Liu
-* run using wsj,MPE
+* run using wsj,MPE  --Chao Liu
 ==== Mic-Array ====
@@ 第17行： / 第17行： @@
 * ALPHA>=0, using data generated by reverber toolkit
 * consider theta
 ====RNN-DAE(Deep based Auto-Encode-RNN)====
@@ 第27行： / 第28行： @@
 ===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.
@@ 第35行： / 第37行： @@
 ===Dark knowledge===
-:* Ensemble
+:* Ensemble using 100h dataset to construct diffrernt structures -- Mengyuan
 ::*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  --Mengyuan Zhao
@@ 第43行： / 第45行： @@
 ===bilingual recognition===
-:* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=359 --Zhiyuan Tang
+:* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=359 --Zhiyuan Tang and Mengyuan
 ==Text Processing==