Zxw：/* Speech Processing */

2015-11-23T07:57:02Z

‎Speech Processing

Zxw：以“==Speech Processing == === AM development === ==== Environment ==== * in disaster ==== RNN AM==== train monophone RNN --zhiyuan : end to end MPE :* http://192.16...”为内容创建页面

2015-11-23T06:52:13Z

以“==Speech Processing == === AM development === ==== Environment ==== * in disaster ==== RNN AM==== *train monophone RNN --zhiyuan :* end to end MPE :* http://192.16...”为内容创建页面

新页面

==Speech Processing ==
=== AM development ===

==== Environment ====
* in disaster

==== RNN AM====
*train monophone RNN --zhiyuan
:* end to end MPE
:* http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=446
* train RNN MPE using large dataset--mengyuan
:*hold
:* better mpe result observed ,unknown errors in previous lstm mpe compiling kaldi
:*http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=403

====Adapative learning rate method====
* sequence training -Xiangyu
:*http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=458

==== Mic-Array ====
* hold
* compute EER with kaldi

====Data selection unsupervised learning====
* hold
* acoustic feature based submodular using Pingan dataset --zhiyong
* write code to speed up --zhiyong
* curriculum learning --zhiyong

====RNN-DAE(Deep based Auto-Encode-RNN)====
* hold
* RNN-DAE has worse performance than DNN-DAE because training dataset is small
* extract real room impulse to generate WSJ reverberation data, and then train RNN-DAE

===Ivector&Dvector based ASR===
* learning from ivector --Lantian
:* CNN ivector learning
:* DNN ivector learning
* binary ivector
* metric learning
* LDA-vector Transfer Learning
* write a technique report

===language vector===
* write a paper--zhiyuan
:*hold
* language vector is added to multi hidden layers--zhiyuan
:* write code done
:* check code
:*http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=480
* RNN language vector
:*hold

===multi-GPU===
* multi-stream training --Sheng Su
:* write a technique report
* kaldi-nnet3 --Xuewei
:* 7*2048 8k 1400h tdnn training Xent done
:* nnet3 mpe code is under investigation
:*http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=472
* train 7*2048 tdnn using 4000h data --Mengyuan

===multi-task===
* test according to selt-information neural structure learning --mengyuan
:* hold
:* write code done
:* no significant performance improvement observed
* speech rate learning --xiangyu
:* no significant performance improvement observed
:*http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=483
: test using extreme data

==Text Processing==
====RNN LM====
*character-lm rnn(hold)
*lstm+rnn
:* check the lstm-rnnlm code about how to Initialize and update learning rate.(hold)

====Neural Based Document Classification====
* (hold)

====RNN Rank Task====
:* Test.
:*Paper: RNN Rank Net.
* (hold)
* Output rank information.

====Graph RNN====
:* Entity path embeded to entity.
*(hold)

====RNN Word Segment====
:* Set bound to word segment.
* (hold)

====Seq to Seq(09-15)====
* Review papers.
* Reproduce baseline. (08-03 <--> 08-17)

====Order representation ====
* Nested Dropout
:*semi-linear --> neural based auto-encoder.
* modify the objective function(hold)

====Balance Representation====
* Find error signal

====Recommendation====
* Reproduce baseline.
:*LDA matrix dissovle.
:* LDA (Text classification & Recommendation System) --> AAAI

====RNN based QA====
*Read Source Code.
*Attention based QA.
*Coding.

====RNN Poem Process====
:*Seq based BP.
*(hold)

===Text Group Intern Project===
====Buddhist Process====
:*(hold)

====RNN Poem Process====
*Done by Haichao yu & Chaoyuan zuo Mentor : Tianyi Luo.

====RNN Document Vector====
:*(hold)

====Image Baseline====
:*Demo Release.
:*Paper Report.
*Read CNN Paper.

===Text Intuitive Idea===
====Trace Learning====
* (Hold)
====Match RNN ====
* (Hold)

=financial group=
==model research==
* RNN
:* online model, update everyday
:* modify cost function and learning method
:* add more feature
==rule combination==
* GA method to optimize the model

==basic rule==
* classical tenth model
==multiple-factor==
* add more factor
* use sparse model
==display==
* bug fixed
:* buy rule fixed
==data==
* data api
:* download the future data and factor data

@@ 第8行： / 第8行： @@
 *train monophone RNN --zhiyuan
 :* end to end MPE
 :* http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=446
-* train RNN MPE using large dataset--mengyuan
-:*hold
-:* better mpe result observed ,unknown errors in previous lstm mpe compiling kaldi
-:*http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=403
 ====Adapative learning rate method====
 * sequence training -Xiangyu
 :*http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=458
@@ 第67行： / 第66行： @@
 :* no significant performance improvement observed
 * speech rate learning --xiangyu
 :* no significant performance improvement observed
 :*http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=483

ASR:2015-11-23 - 版本历史

Zxw：/* Speech Processing */

Zxw：以“==Speech Processing == === AM development === ==== Environment ==== * in disaster ==== RNN AM==== *train monophone RNN --zhiyuan :* end to end MPE :* http://192.16...”为内容创建页面

Zxw：以“==Speech Processing == === AM development === ==== Environment ==== * in disaster ==== RNN AM==== train monophone RNN --zhiyuan : end to end MPE :* http://192.16...”为内容创建页面