ASR:2015-09-21 - 版本历史

Zxw：/* Speech Processing */

2015-09-21T09:00:09Z

‎Speech Processing

Zxw：/* Speech Processing */

2015-09-21T06:51:32Z

‎Speech Processing

Zxw：/* Speech Processing */

2015-09-21T06:50:59Z

‎Speech Processing

Zhaomy：/* multi-GPU= */

2015-09-21T04:43:20Z

‎multi-GPU=

Zhaomy：/* language vector */

2015-09-21T04:42:21Z

‎language vector

Xingchao：以“==Speech Processing == === AM development === ==== Environment ==== * grid-12 GPU is transferred to grid-18 * buy a 970 GPU ==== RNN AM==== *train monophone RNN --...”为内容创建页面

2015-09-21T01:34:47Z

以“==Speech Processing == === AM development === ==== Environment ==== * grid-12 GPU is transferred to grid-18 * buy a 970 GPU ==== RNN AM==== *train monophone RNN --...”为内容创建页面

新页面

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

==== Environment ====
* grid-12 GPU is transferred to grid-18
* buy a 970 GPU

==== RNN AM====
*train monophone RNN --zhiyuan
:* decode using 5-gram
:* the train method of batch
* train using large dataset--mengyuan
* write code to tune learning rate --zhiyong
:* has completed Nestrov/Adagrad/Adagrad-max
:* has unstable phenomenon
:* completed adam,adadeta,adam-max --Xiangyu,Zhiyong
:* reproduce PSO --Xiangyu

==== 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)====
* 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===
* Cluster the speakers to speaker-cluster
:* hold
* dark knowledge
:* has much worse performance than baseline (EER: base 29% dark knowledge 48%)
* RNN ivector
:* hold
* binary ivector done
* metric learning

===language vector===
* hold
* train using language vector with the dataset of 1400h_CN + 100h_EN--mengyuan
* write a paper--zhiyuan
* RNN language vector
:* hold

===multi-GPU====
* multi-stream training --Sheng Su
:* two GPUs work well, but four GPUs divergent
* solve the problem of buffer--Mengyuan, Sheng Su

===Neutral picture style transfer===
* reproduced the result of the paper "A neutral algorithm of artistic style" --Zhiyuan, Xuewei
* while subject to the GPU's memory, limited to inception net with sgd optimizer (VGG network with the default L-BFGS optimizer consumes very much memory, which is better)

==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4行： / 第4行： @@
 ==== Environment ====
 * grid-12 GPU is transferred to grid-18
-* buy a 970 GPU
+* grid-14 is unstable
 ==== RNN AM====
 *train monophone RNN --zhiyuan
 :* decode using 5-gram
 :* the train method of batch
-* train using large dataset--mengyuan
+:* test using another test set
-* write code to tune learning rate --zhiyong
+* train RNN MPE using large dataset--mengyuan
-:* has completed Nestrov/Adagrad/Adagrad-max
+:* diverge problem
 :* has unstable phenomenon
-:* completed adam,adadeta,adam-max --Xiangyu,Zhiyong
+* completed adam,adadeta,adam-max --Xiangyu,Zhiyong
-:* reproduce PSO --Xiangyu
+* reproduce PSO --Xiangyu
 ==== Mic-Array ====
@@ 第25行： / 第29行： @@
 * acoustic feature based submodular using Pingan dataset --zhiyong
 * write code to speed up --zhiyong
 ====RNN-DAE(Deep based Auto-Encode-RNN)====
@@ 第51行： / 第56行： @@
 * solve the problem of buffer-- Sheng Su
-===Neutral picture style transfer===
+===Neutral picture style transfer==
 * reproduced the result of the paper "A neutral algorithm of artistic style" --Zhiyuan, Xuewei
 * while subject to the GPU's memory, limited to inception net with sgd optimizer (VGG network with the default L-BFGS optimizer consumes very much memory, which is better)
 ==Text Processing==

@@ 第21行： / 第21行： @@
 * compute EER with kaldi
-===Data selection unsupervised learning
+====Data selection unsupervised learning====
 * hold
 * acoustic feature based submodular using Pingan dataset --zhiyong

@@ 第49行： / 第49行： @@
 * multi-stream training --Sheng Su
 :* two GPUs work well, but four GPUs divergent
-* solve the problem of buffer--Mengyuan, Sheng Su
+* solve the problem of buffer-- Sheng Su
 ===Neutral picture style transfer===

@@ 第42行： / 第42行： @@
 ===language vector===
 * hold
 * write a paper--zhiyuan
 * RNN language vector
 :* hold
 ===multi-GPU====