“ASR:2015-08-10”版本间的差异

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Speech Processing
 
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* grid-14 is on repairation
 
* grid-14 is on repairation
 
* prepare to buy a server
 
* prepare to buy a server
 
  
 
==== RNN AM====
 
==== RNN AM====
 
*hold  
 
*hold  
*morpheme RNN --zhiyuan
+
*train morpheme RNN --zhiyuan
 
*train using 1400h large dataset--mengyuan
 
*train using 1400h large dataset--mengyuan
*write code to tune learning rate
+
*write code to tune learning rate--zhiyong
  
 
==== Mic-Array ====
 
==== Mic-Array ====
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====Data selection unsupervised learning
 
====Data selection unsupervised learning
 
* hold
 
* hold
* acoustic feature based submodular using Pinan dataset --zhiyong
+
* acoustic feature based submodular using Pingan dataset --zhiyong
 
* write code to speed up --zhiyong
 
* write code to speed up --zhiyong
  
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====RNN-DAE(Deep based Auto-Encode-RNN)====
 
====RNN-DAE(Deep based Auto-Encode-RNN)====
 
* hold
 
* hold
* deliver to mengyuan
+
* deliver to mengyuan, xuewei
 
:* 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
 
   
 
   
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===monophone===
 
===monophone===
 
* hold  
 
* hold  
* triphone is tranfered to monophone
+
* triphone is tranfered to monophone--zhiyong
 +
 
 +
===multi-GPU====
 +
* multi-stream training --Sheng Su
  
 
==Text Processing==
 
==Text Processing==

2015年8月10日 (一) 07:51的最后版本

Speech Processing

AM development

Environment

  • grid-14 is on repairation
  • prepare to buy a server

RNN AM

  • hold
  • train morpheme RNN --zhiyuan
  • train using 1400h large dataset--mengyuan
  • write code to tune learning rate--zhiyong

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


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

  • hold
  • deliver to mengyuan, xuewei

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

language vector

  • hold
  • train using language vector with the dataset of 1400h_CN + 100h_EN--mengyuan
  • hold
  • write a paper--zhiyuan
  • RNN language vector
  • train as a paper--xuewei

rectifier

  • hold
  • rectifier RNN --zhiyuan

monophone

  • hold
  • triphone is tranfered to monophone--zhiyong

multi-GPU=

  • multi-stream training --Sheng Su

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

  • Paper: RNN Rank Net.
  • (hold)

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)

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.
  • (hold)

RNN Poem Process

  • Seq based BP.

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

tonglian platform

  • learn the platform
  • arma,ar,boosting tree is done

strategy

  • rule optimize model
  • Adaboost method
  • technology index
  • survey the current technology index
  • NN
  • RNN model using Theano

display platform

  • set up the test platform in our grid