“ASR:2015-06-15”版本间的差异

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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
  
* ==== Mic-Array ====
+
==== Mic-Array ====
 
* hold  
 
* 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
 
* compute EER with kaldi
  
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===Speaker ID===   
 
===Speaker ID===   
*  DNN-based sid --Tian Lan
+
*  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===
* 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.
+
* dark-konowlege using i-vector
:* 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)
 
* train on wsj(testbase dev93+evl92)
 +
:*--hold
  
 
===Dark knowledge===
 
===Dark knowledge===
* 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 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
 
 
* test random last output layer when train MPE--zhiyuan
 
* test random last output layer when train MPE--zhiyuan
  
===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
 
  
 
===language vector===
 
===language vector===
* train DNN with language vector--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