“ASR:2015-05-18”版本间的差异

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==== Environment ====
 
==== Environment ====
 
* grid-15 often does not work
 
* grid-15 often does not work
 +
* grid-14 often does not work
  
 
==== RNN AM====
 
==== RNN AM====
第10行： 第11行：
 
* run using wsj,MPE  --Chao Liu
 
* run using wsj,MPE  --Chao Liu
 
* run bi-directon --Chao Liu   
 
* run bi-directon --Chao Liu   
−
* modify code --Zhiyuan
+
* train RNN with dark knowledge transfer on AURORA4 --zhiyuan
 +
:*http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=383--zhiyuan
  
 
==== Mic-Array ====
 
==== Mic-Array ====
 +
* hold
 
* Change the prediction from  fbank to spectrum features
 
* 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  
第20行： 第23行：
  
 
====RNN-DAE(Deep based Auto-Encode-RNN)====
 
====RNN-DAE(Deep based Auto-Encode-RNN)====
−
* HOLD --Zhiyong Zhang
+
* deliver to mengyuan
−
* 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 Lin
+
* 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&Dvector based ASR===
 
===Ivector&Dvector based ASR===
−
*  hold     --Tian Lan  
+
*  hold --Tian Lan  
−
:* Cluster the speakers to speaker-classes, then using the distance or the posterior-probability as the metric
+
* 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.
+
* 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
−
:* Augument to hidden layer is better than input layer
+
* Augument to hidden layer is better than input layer
−
:* train on wsj(testbase dev93+evl92)
+
* train on wsj(testbase dev93+evl92)
  
 
===Dark knowledge===
 
===Dark knowledge===
−
:* Ensemble using 100h dataset to construct diffrernt structures -- Mengyuan
+
* 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
+
:*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
+
* adaptation English and Chinglish
−
::* Try to improve the chinglish performance extremly
+
:* Try to improve the chinglish performance extremly
−
:* unsupervised training with wsj contributes to aurora4 model --Xiangyu Zeng
+
* unsupervised training with wsj contributes to aurora4 model --Xiangyu Zeng
−
::* test large database with AMIDA
+
* test large database with AMIDA
 +
* test hidden layer knowledge transfer--xuewei
  
 
===bilingual recognition===
 
===bilingual recognition===
 +
* hold
 
:* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=359 --Zhiyuan Tang and Mengyuan
 
:* http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=359 --Zhiyuan Tang and Mengyuan
 +
 +
===language vector===
 +
* train DNN with language vector--xuewei
  
 
==Text Processing==
 
==Text Processing==
第57行： 第65行：
 
* CNN adapt to resolve the low resource problem
 
* CNN adapt to resolve the low resource problem
 
===Translation===
 
===Translation===
−
* similar-pair method in English word using translation model.
+
*Test the performance of the similar-pair method in bilingual recognition
−
:* result:wer:70%-50% on top1.
+
−
:* change the AM model
+
  
 
===Order representation ===
 
===Order representation ===
 
* modify the objective function
 
* modify the objective function
 
* sup-sampling method to solve the low frequence word
 
* sup-sampling method to solve the low frequence word
 +
* Sort out vectors and do the experiment on objective function convergence
 +
* test on classification task and prediction task
 +
 
===binary vector===
 
===binary vector===
 +
*Finish hamming metric binary vector.
 +
*Try to finish binary vector.
 +
*Do  test report.
  
 
===Stochastic ListNet===
 
===Stochastic ListNet===
−
* using sampling method and test
+
*To finish writing first edition of emnlp 2015 long paper
  
 
===relation classifier===
 
===relation classifier===
−
* test the bidirectional neural network(B-RNN) and get a little improvement
+
*Tune the best model.
 +
*Train on new wordembedding.
 +
*Do some analysis(length of context, track the pooling.)
 +
*Finish the draft.
  
 
===plan to do===
 
===plan to do===
 
* combine LDA with neural network
 
* combine LDA with neural network

2015年5月25日 (一) 00:59的最后版本

Speech Processing

AM development

Environment

  • grid-15 often does not work
  • grid-14 often does not work

RNN AM

  • details at http://liuc.cslt.org/pages/rnnam.html
  • Test monophone on RNN using dark-knowledge --Chao Liu
  • run using wsj,MPE --Chao Liu
  • run bi-directon --Chao Liu
  • train RNN with dark knowledge transfer on AURORA4 --zhiyuan

Mic-Array

  • hold
  • 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
  • compute EER with kaldi

RNN-DAE(Deep based Auto-Encode-RNN)

  • deliver to mengyuan

Speaker ID

  • DNN-based sid --Yiye Lin

Ivector&Dvector based ASR

  • hold --Tian Lan
  • 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.
  • 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 using 100h dataset to construct diffrernt structures -- Mengyuan
  • adaptation English and Chinglish
  • Try to improve the chinglish performance extremly
  • unsupervised training with wsj contributes to aurora4 model --Xiangyu Zeng
  • test large database with AMIDA
  • test hidden layer knowledge transfer--xuewei

bilingual recognition

  • hold

language vector

  • train DNN with language vector--xuewei

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

  • make a technical report about document classification using CNN --yiqiao
  • CNN adapt to resolve the low resource problem

Translation

  • Test the performance of the similar-pair method in bilingual recognition

Order representation

  • modify the objective function
  • sup-sampling method to solve the low frequence word
  • Sort out vectors and do the experiment on objective function convergence
  • test on classification task and prediction task

binary vector

  • Finish hamming metric binary vector.
  • Try to finish binary vector.
  • Do test report.

Stochastic ListNet

  • To finish writing first edition of emnlp 2015 long paper

relation classifier

  • Tune the best model.
  • Train on new wordembedding.
  • Do some analysis(length of context, track the pooling.)
  • Finish the draft.

plan to do

  • combine LDA with neural network
取自“http://cslt.org/mediawiki/index.php?title=ASR:2015-05-18&oldid=15207”