2015年12月1日 (二) 07:31的版本 （查看源代码） Zxw（讨论 | 贡献） （→‎Text Processing） ←上一编辑		2015年12月7日 (一) 07:21的最后版本 （查看源代码） Zxw（讨论 | 贡献） （→‎Speech Processing）
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	==Speech Processing ==		==Speech Processing ==
		+
	=== AM development ===		=== AM development ===
	==== Environment ====		==== Environment ====
−	==== RNN AM====	+	==== End-to-End ====
−	*train monophone RNN --zhiyuan	+	*monophone ASR --Zhiyuan
−	:* end to end MPE	+	:* MPE
−	:* end to end using nnet3	+	:* CTC/nnet3/Kaldi
−	:* http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=446	+
		+	==== conditioning learning ====
		+	* language vector into multiple layers --Zhiyuan
		+	:* a Chinese paper
		+	* speech rate into multiple layers --Zhiyuan
		+	:*verify the code for extra input(s) into DNN
	====Adapative learning rate method====		====Adapative learning rate method====
	* sequence training -Xiangyu		* sequence training -Xiangyu
	:* write a technique report		:* write a technique report
−	:*http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=458
	==== Mic-Array ====		==== Mic-Array ====
第31行：		第35行：
	* extract real room impulse to generate WSJ reverberation data, and then train RNN-DAE		* extract real room impulse to generate WSJ reverberation data, and then train RNN-DAE
−	===Ivector&Dvector based ASR===	+	===Speaker recognition===
−	* learning from ivector --Lantian	+	* DNN-ivector framework
−	:* CNN ivector learning	+	* SUSR
−	:* DNN ivector learning	+	* AutoEncoder + metric learning
	* binary ivector		* binary ivector
−	* metric learning
−	* LDA-vector Transfer Learning
−
	===language vector===		===language vector===
第49行：		第50行：
	* RNN language vector		* RNN language vector
	:*hold		:*hold
−	* train with extra input of speech rate info	+
	===multi-GPU===		===multi-GPU===
第59行：		第60行：
	:*http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=472		:*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		* train 7*2048 tdnn using 4000h data --Mengyuan
		+	* 1700h+776h 16k nnet3 6*2000 training done, outperform 6776H_mpe model--Mengyuan
		+	* wrote nnet3 biglm-decoder for sinovoice.
	* train mpe using wsj and aurara4 --Zhiyong,Xuewei		* train mpe using wsj and aurara4 --Zhiyong,Xuewei
		+	* train nnet3 mpe using data from Jietong--Xuewei
	===multi-task===		===multi-task===
第70行：		第74行：
	:* no significant performance improvement observed		:* no significant performance improvement observed
	:*http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=483		:*http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=483
−	: test using extreme data	+	: get results with extra input of speech rate info --Zhiyuan
	==Text Processing==		==Text Processing==
−	/*	+	===Work===
−	====RNN LM====	+	====RNN Poem Process====
−	*character-lm rnn(hold)	+	* Combine addition rhyme.
−	*lstm+rnn	+	* Investigate new method.
−	:* check the lstm-rnnlm code about how to Initialize and update learning rate.(hold)	+	====Document Represent====
−	*/	+	* Code done. Wait some experiments result.
		+	====Seq to Seq====
		+	* Work on some tasks.
		+	====Order representation ====
		+	* Code some idea.
		+	====Balance Representation====
		+	* Investigate some papers.
		+	* Current solution : Use knowledge or large corpus's similar pair.
		+
		+	===Hold===
	====Neural Based Document Classification====		====Neural Based Document Classification====
−	* (hold)
−
	====RNN Rank Task====		====RNN Rank Task====
−	:* Test.
−	:*Paper: RNN Rank Net.
−	* (hold)
−	* Output rank information.
−
	====Graph RNN====		====Graph RNN====
	:* Entity path embeded to entity.		:* Entity path embeded to entity.
	*(hold)		*(hold)
−
	====RNN Word Segment====		====RNN Word Segment====
	:* Set bound to word segment.		:* Set bound to word segment.
	* (hold)		* (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====		====Recommendation====
	* Reproduce baseline.		* Reproduce baseline.
	:*LDA matrix dissovle.		:*LDA matrix dissovle.
	:* LDA (Text classification & Recommendation System) --> AAAI		:* LDA (Text classification & Recommendation System) --> AAAI
−
	====RNN based QA====		====RNN based QA====
	*Read Source Code.		*Read Source Code.
	*Attention based QA.		*Attention based QA.
	*Coding.		*Coding.
−
−	====RNN Poem Process====
−	:*Seq based BP.
−	*(hold)
	===Text Group Intern Project===		===Text Group Intern Project===

---

2015年12月7日 (一) 07:21的最后版本

Speech Processing

AM development

Environment

End-to-End

• monophone ASR --Zhiyuan

• MPE
• CTC/nnet3/Kaldi

conditioning learning

• language vector into multiple layers --Zhiyuan

• a Chinese paper

• speech rate into multiple layers --Zhiyuan

• verify the code for extra input(s) into DNN

Adapative learning rate method

• sequence training -Xiangyu

• write a technique report

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

Speaker recognition

• DNN-ivector framework
• SUSR
• AutoEncoder + metric learning
• binary ivector

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
• 1700h+776h 16k nnet3 6*2000 training done, outperform 6776H_mpe model--Mengyuan
• wrote nnet3 biglm-decoder for sinovoice.
• train mpe using wsj and aurara4 --Zhiyong,Xuewei
• train nnet3 mpe using data from Jietong--Xuewei

multi-task

• test according to selt-information neural structure learning --mengyuan

• hold
• write code done
• no significant performance improvement observed

• speech rate learning --xiangyu

• hold
• no significant performance improvement observed
• http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=483

get results with extra input of speech rate info --Zhiyuan

Text Processing

Work

RNN Poem Process

• Combine addition rhyme.
• Investigate new method.

Document Represent

• Code done. Wait some experiments result.

Seq to Seq

• Work on some tasks.

Order representation

• Code some idea.

Balance Representation

• Investigate some papers.
• Current solution : Use knowledge or large corpus's similar pair.

Hold

Neural Based Document Classification

RNN Rank Task

Graph RNN

• Entity path embeded to entity.

• (hold)

RNN Word Segment

• Set bound to word segment.

• (hold)

Recommendation

• Reproduce baseline.

• LDA matrix dissovle.
• LDA (Text classification & Recommendation System) --> AAAI

RNN based QA

• Read Source Code.
• Attention based QA.
• Coding.

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